Methods and magnetic imaging devices to inventory human brain cortical function

ABSTRACT

Techniques are described for determining cognitive impairment, an example of which includes accessing a set of epochs of magnetoencephalography (MEG) data of responses of a brain of a test patient to a plurality of auditory stimulus events; processing the set of epochs to identify parameter values one or more of which is based on information from the individual epochs without averaging or otherwise collapsing the epoch data. The parameter values are input into a model that is trained based on the parameters to determine whether the test patient is cognitively impaired.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/151,105, filed Oct. 3, 2018, now U.S. Pat. No. 11,337,631, whichclaims priority to U.S. Provisional Application Ser. No. 62/567,708,filed Oct. 3, 2017, entitled “Brain F.I.T. (Frequency, Intensity, andTiming) Test for Cognitive Assessment”, which is hereby incorporatedherein by reference in its entirety.

FIELD OF THE ART

The present description is directed to the field of medical imaging.More particularly, this description pertains to systems and methods ofdetecting and evaluating electromagnetic activity in the brain.

BACKGROUND

Despite rapidly increasing societal burden, progress in developingtreatments for neurodegenerative disorders, such as Alzheimer's disease(“AD”), remains slow.

Part of the challenge in developing effective therapeutic agents is therequirement that the molecule cross the blood-brain barrier (“BBB”) inorder to engage a disease-relevant target. Another challenge,particularly relevant to efforts to develop disease-modifying agents, isthe need for non-invasive techniques that can repeatedly be used tomonitor disease status and progression. Although several imagingapproaches have been used to monitor efficacy of potentialdisease-modifying antibodies in AD clinical trials—notably positronemission tomography (“PET”) detection of β-amyloid plaque burden—theseradioisotopic imaging techniques detect a presumptive pathophysiologicalcorrelate of disease and do not directly measure the primary symptom,the loss of cognitive function.

Existing approaches to measuring brain function are likewise poorlysuited to monitoring neurodegenerative disease status and progression.

Cerebral cortex functional imaging approaches currently in clinical usedo not image neural function directly: functional magnetic resonanceimaging (“fMRI”) images blood flow; positron emission tomography(“PET”), when used to monitor glucose consumption, images metabolism.

In addition, there can be a mismatch between the temporal resolution ofcertain functional imaging approaches and the duration of signalingevents in the brain. fMRI, for example, is sensitive on a time frame ofseconds, but normal events in the brain occur in the time frame ofmilliseconds (“msec”). Although electroencephalography (“EEG”) issensitive to events in a millisecond time frame, unpredictable signalattenuation by the tissues that surround the brain cause both near andfar signals to be comingled. This problem is compounded when there aremultiple current sources (e.g., both primary and secondary corticalsources).

There thus exists a need in the art for noninvasive techniques forimaging brain cortical function that can be used to detect and monitorchanges in function. There is a particular need for noninvasivefunctional imaging approaches that can be used to detect, stage, andmonitor progression of neurodegenerative disorders with statisticallysignificant classification accuracy.

SUMMARY

We have discovered that past failures in using magnetoencephalography(“MEG”) to detect cognitive impairment (CI) were due to the conflationof evoked responses to a repeated stimulus. Previously, it was common toaverage the evoked response over all such stimuli, and compare theaveraged evoked responses between individuals. These initial resultsindicated that a single parameter based on conflated MEG data may not besufficient to differentiate all normal test patients from allcognitively-impaired test patients based on the model MEG data. We havediscovered that statistically meaningful differences between normal anddiseased brain responses to a repeated stimulus are found in therelative presence and intensity of certain parameters in an individual'sevoked responses across multiple distinct evoked responses; thisdistributional information has previously been discarded in an earlystep of signal analysis through averaging of those responses.

Accordingly, we have now developed models that are capable ofnoninvasively detecting, staging, and monitoring progression ofneurodegenerative disorders with statistically significantclassification accuracy.

The models separate patients having a cognitive dysfunction frompatients with a normal cognitive function based on test MEG datacollected from test patients' brain activity. The models are developedby collecting model MEG data from a pool of test patients having a rangeof cognitive function states that have been preferably objectivelyevaluated by an alternative protocol such as the Mini Mental State Exam(“MMSE”). The model MEG data is collected using at least onesuperconducting quantum interference device (“SQUID”) sensor detectingsignals from the brain of test patients under a data collectionprotocol. The MEG measures the relative extent of brain activation,excitation, and/or response. The MEG data from at least one SQUIDsensors, generally no more than one, or generally no more than ahandful, is subsequently analyzed. Candidate parameters in the form ofdifferences between the MEG scans of dysfunctional test patients andnormal test patients are identified. The candidate parameters aredeveloped to quantify these differences and to show that the activation,excitation, and/or response occurs progressively differently withprogressive cognitive dysfunction. Specific ones of the candidateparameters are then selected for inclusion in one of the models as modelparameters. Data science techniques of varying complexity, fromregressions to machine learning and deep learning algorithms, are usedto train the model for use in recognizing, quantifying, and categorizingpatients outside the test set.

As a specific example, a CI model is able to separate test patients withnormal cognitive function from those with cognitive dysfunctioncharacteristic as measured by one or more psychiatric tests. To trainthe models, MEG with a conventional set of SQUID sensors is used todetect signals from the brain following an auditory stimulus in a set oftest patients. The test patients have a range of cognitive functionstates that have been preferably objectively evaluated by an alternativeprotocol. The MEG measures, after an auditory stimulus, the relativeextent of brain activation/excitation and subsequent response to theactivation. Subtle differences between the MEG scans of CI test patientsand “normal” (NV) test patients were identified. Discrete candidateparameters of the model MEG data were identified as model parameters andwere developed to quantify these subtle differences. The models andtheir constituent model parameters have been shown to robustlydistinguish between normal and CI patients, with performance varyingfrom perfect categorization of the test patients downward depending onhow many model parameters are used. In implementation, models may bebuilt from among a range of possible model parameters, whichconcordantly have a range of performance in ability to distinguishnormal and CI patients.

Other features and advantages of the present invention will be apparentfrom the following more detailed description, taken in conjunction withthe accompanying drawings which illustrate, by way of example, theprinciples of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows schematically a test patient in a movable patient supportdevice for a magnetoencephalography (“MEG”) system in one embodiment.

FIG. 1B shows schematically a top view of an example sensor head with anarray of superconducting quantum interference device (“SQUID”) sensorswith the five surrounding sensors focused to an area about two to fourcentimeters below the central sensor in one embodiment.

FIG. 1C shows a cross section of the SQUID sensor head of FIG. 1B alongline 33 with the sensor head oriented to detect a magnetic fieldgenerated by electrical signals near a sulcus of a brain in oneembodiment.

FIG. 1D shows a logical component diagram of an MEG system in oneembodiment.

FIG. 2A shows example averaged responses to a stimulus for each of anumber of SQUID sensors.

FIG. 2B shows an example averaged response for a single SQUID sensor.

FIG. 3A shows an example heat map of the epochs of amagnetoencephalography (“MEG”) set of scans from a single session for asingle SQUID sensor for a first normal patient.

FIG. 3B shows an example heat map of the epochs of a MEG set of scansfrom a single session for a single SQUID sensor for an Alzheimer'sDisease (“AD”) patient.

FIG. 3C shows an example heat map of the epochs of a MEG set of scansfrom a single session for a second normal patient.

FIG. 3D shows a procedure for estimating the candidate parameter nB.

FIG. 3E shows an example Bland-Altman reliability plot for the candidateparameter A*B*C for an example set of test patients.

FIG. 3F shows an example Bland-Altman stability plot for the candidateparameter A*B*C for an example set of test patients.

FIG. 4A shows schematically a gradiometer and magnetometer orientationof SQUID sensors in one embodiment.

FIG. 4B shows example response signals from three different sessions ona representative normal patient.

FIG. 4C shows example response signals from three different testsessions on an AD patient.

FIG. 4D shows the mean and standard deviation of the Pearson r value asa function of the number of candidate parameters used in an ADD model.

FIG. 4E shows the mean and standard deviation of the classificationaccuracy as a function of the number of candidate parameters used in anADD model.

FIG. 5 shows an example graphical user interface for presentation on acomputer display to provide results from use of an ADD model on a testpatient.

FIG. 6A shows separation of patients by patient group for a linear ADDmodel of seven model parameters.

FIG. 6B shows separation of patients by summed Mini-Mental StateExamination (“MMSE”) score for the linear ADD model associated with FIG.6A.

FIG. 6C shows separation of patients by patient group for a non-linearADD model of eight model parameters.

FIG. 6D shows separation of patients by summed MMSE score for thenon-linear ADD model associated with FIG. 6C.

FIG. 7 illustrates a correlation matrix between ipsilateral features(vertical) and different psychiatric tests for evaluating cognitiveimpairment (horizontal), according to one embodiment.

FIGS. 8A, 8B, and 8C illustrate scatterplots of within-day featurevariability for three possible model features, according to oneembodiment.

FIG. 9 illustrates a scatterplot of one such example feature where theaverage onset of the B peak shows an inverse correlation with apatient's MMSE score, according to one embodiment.

FIG. 10 illustrates a correlation matrix between contralateral features(vertical) and different psychiatric tests (horizontal), according toone embodiment.

FIGS. 11A and 11B plot predicted and actual MMSE scores for two types ofdual-channel CI models, according to one embodiment.

FIG. 12 illustrates a graphical user interface for presenting theresults of scans and the prediction of a CI model, according to oneembodiment.

Wherever possible, the same reference numbers will be used throughoutthe drawings to represent the same parts.

DETAILED DESCRIPTION I. Measurement Setup

FIG. 1A shows a Magnetoencephalography (“MEG”) system 48 to detectelectrical activity in the human brain, in the form of the magneticfields generated by the electrical activity, according to oneembodiment. A test patient 50 is seated in a patient support device 14.A Faraday cage 10 surrounds the test patient 50 and the patient supportdevice 14 to block external environmental magnetic fields. The sensorhead 12 and the associated Dewar housing 40 (see FIG. 1C) to cool thesensors 32 (see FIG. 1B) are fixed in space. The sensor head 12 and thepatient support device 14 are in communication with and controlled by acomputer 20, which is located outside the Faraday cage 10.

The patient support device 14 includes a seat portion 16 and a backportion 18. The patient support device 14 is rotatable 22 at least afull 360°, with the back portion 18 being reclinable 24, preferably froma vertical position to a position about 45° from vertical. The patientsupport device 14 is also controlled horizontally 26 and vertically 28in order to maintain the sensor head 12 in contact with the head of thetest patient 50, as the angle of inclination of the patient supportdevice back 18 is simultaneously changed or the patient support device14 is simultaneously rotated. The patient support device 14 alsoincludes a head stabilizer 30 to maintain the head in a predeterminedfixed position with respect to the patient support device back 18. Thehead stabilizer 30 contacts the cheeks of the test patient 50 toimmobilize the cheek bones, thereby immobilizing the head.

The vertical, horizontal, rotational, and recline adjustments to thepatient support device 14 may be automated and controlled by thecomputer 20. Alternatively, the adjustments may be manual or automatedby the patient support device 14 itself. The SQUID electronics includesa monitor and a computer 20 with software for operation of the SQUIDsensors 32 and control of the position of the patient support device 14.If the vertical, horizontal, rotational, and recline adjustments aredone manually or independently of the computer 20, a location sensor maybe used to determine the location of the head surface of the testpatient 50 with respect to the SQUID sensors 32.

FIG. 1B shows a top view of an example SQUID sensor head 12 with fiveSQUID sensors 32 in an array around a sixth central SQUID sensor 32,according to one embodiment. The central SQUID sensor 32 is flat withthe five surrounding SQUID sensors 32 oriented at a fixed angle towardthe central SQUID sensor 32. The fixed angle in FIG. 1B is about 45°. Inother embodiments, other counts, orientations, and relative arrangementsof SQUID sensors 32 may be used.

Although the measurement setup may comprise a currently manufactured MEGdevice such as an Elekta Neuromag® 306 channel (306 MEG sensor) MEGdevice with associated other hardware, the measurement setup mayalternatively be an MEG device comprising fewer sensors and a relativelysimplified measurement setup as will be further described below. This isadvantageous for numerous reasons, one of which is cost. An ElektaNeuromag® 306 channel setup costs $2,000,000 at the time of thiswriting, whereas one embodiment of the simplified measurement setupwould only cost approximately $200,000 at the time of this writing.

In some embodiments of a simplified measurement setup, the systempreferably uses a single wire Faraday cage 10 for magnetic isolation.The Faraday cage 10 is a wire enclosure formed by a mesh of conductingmaterial and blocks external static and non-static electric fields bycanceling out their effects on the interior of the Faraday cage 10. TheFaraday cage 10 surrounds the test patient 50 and sensor head 12.

In some embodiments of a simplified measurement setup, relatively fewSQUID sensors 32, down to as few as a single sensor, are used, whichreduces the equipment cost. One, two, three, four, five, six, seven,eight, or nine sensors may be used. In some embodiments, a movablepatient support device 14, movable manually or by a software program, isused in conjunction with the relatively small array of SQUID sensors 32.This allows the brain region of interest (desired to be analyzed) to beprecisely determined and defined (e.g., the superior temporal gyms).This helps ensure that those few SQUID sensors that are used are placedat a location around the brain identified as generating the signalsdesired to be analyzed. The small array SQUID sensor head 12 is lower incost not only because of the reduced sensor count, but also because ofcommensurately reduced volume of liquid helium in a stationary Dewarhousing 40 (see FIG. 1C) relative to the movable Dewar housing of, forexample, the Elekta Neuromag® 306 system or equivalents. Further, byhaving SQUID sensors 32 that are not constrained to discrete, fixedlocations with respect to the head of the test patient 50, the systemdescribed herein may also be able to provide significantly better imagesof the cortical region of interest relative to the more expensivesystem.

The patient support device 14 is non-magnetic and non-paramagnetic(ideally completely of plastic components) to prevent any interferencewith the SQUID device.

In one specific embodiment, the array of SQUID sensors 32 is fixed at apredetermined angle with respect to vertical. The predetermined angle isabout 50° or less. As a specific example, the array of SQUID sensors 32is fixed at an angle of about 45° from vertical with five SQUID sensors32 at the points of a pentagon, each about 2 cm from a central sixthSQUID sensor 32. Each SQUID sensor 32 is about 1.5 cm in diameter. Theperipheral SQUID sensors 32 are aimed at a point about 2 cm below thecentral SQUID sensor 32. The MEG system 48 includes a Dewar flask with asmall liquid helium reservoir. The test patient 50 sits in the patientsupport device 14 that is tiltable up to about 45° or 50° from verticaland rotatable at least 360°, similar to a dentist chair, but withprecise control of the orientation and tilt of the patient supportdevice 14. The precise location (including both tri-axis position andorientation) of the patient support device 14 is communicated to thesoftware of the computer 20 directing the data acquisition. The patientsupport device 14 stabilizes the head of the test patient 50 by acushioned support on each maxilla. The test patient 50 and sensor head12 are housed completely in a Faraday cage 10 to shield environmentalmagnetic flux. Such a device may be used anywhere, i.e., it is easilyphysically portable between rooms, and is expected to cost only about$200,000 at the time of this writing.

The array of SQUID sensors 32 is placed over the area(s) of interest ofthe brain. The array of SQUID sensors 32 may be placed over the inferiorfrontal gyms to detect the “top down” response from the corticalexecutive region. The latter part of the 500-msec signal over theauditory cortex may likely also capture some of this information. Thesame strategy may be used for visual, sensory, motor, and cognitiveinventory. Data collected from the array of SQUID may be used to createa regional magnetic cortical surface map to inventory the function ofhearing, sight, touch, movement, and cognition of a normal healthybrain. This information may allow the analysis of individuals in diseasestates or other conditions of interest.

Generally, each SQUID sensor 32 in an array may function as an axialgradiometer to attenuate the environmental magnetic noise. The positionof the array of SQUID sensors 32 can be correlated by an imaging of thehead to give a precise location of the array of SQUID sensors 32relative to the brain structures. Any imaging technique may be used thatdistinguishes the physical location and shape of the brain, including,but not limited to ultrasound and magnetic resonance imaging (“MRI”). Inthis case, only detected signals that demonstrate the expected strengthdecay laterally between SQUID sensors 32, consistent with a superficialsignal origin, are scored. Software directs the movable array of SQUIDsensors 32 to refine the image in order to provide a robust surface mapof the surface sulcal activity, thereby specifically creating a map ofbasal neural activity or “noise”.

In another specific embodiment, an array of three to nine or more SQUIDsensors 32, about one centimeter in size with a fixed radial geometry,may be used to image the brain or the surface of the brain via acomputer-directed movable C-arm.

FIG. 1C shows the SQUID sensor head 12 placed against the scalp 52 ofthe test patient 50 above a sulcus 54 of interest, according to oneembodiment. The peripheral SQUID sensors 32 (see also FIG. 1B) and thecentral SQUID sensor 32 converge on a focal point 38 about two to fourcentimeters below the central sensor 32. The sensor head 12 includes aDewar housing 40 for the sensors. The Dewar housing 40 holds the liquidhelium in the enclosed portion 42 of the sensor head 12 to maintain theSQUID sensors 32 at superconducting temperatures and insulates the SQUIDsensors 32 and the liquid helium from the environment and the head ofthe test patient 50. Electrical wiring 44, 46 powers each of the SQUIDsensors 32. The neuronal structures 56, and hence the electricalimpulses, in the sulcal wall are oriented substantially parallel 58 tothe scalp 52, thereby generating a magnetic field 60 in a planesubstantially perpendicular to the scalp 52. In contrast, the neuronalstructures 62, and hence the electrical impulses, of the gyms 64 areoriented substantially perpendicular 66 to the scalp 52, therebygenerating a magnetic field 68 in a plane substantially parallel to thescalp 52. The magnetic field 60 generated from electrical activity inthe sulcus 54 therefore is much more easily detected than the magneticfield 68 generated from electrical activity in the gyrus 64 with thesensor head 12 located as shown in FIG. 1C.

The location of the source of a magnetic signal may be estimated by theSQUID sensors 32, and when the source of the magnetic signal is expectedto be at a sulcus 54, the sulcus 54 location may be estimated directlyfrom the SQUID signals. For example, when the right index finger isstimulated, the SQUID signal maximum is over the left sensory cortex,where sensory input from the finger is registered.

More generally, the sulcus 54 represents a physical boundary and anabsolute limit to current transmission and thus to magnetic fieldtransmission. That is, a SQUID sensor 32 placed contralateral to asulcus-generated signal detects signals from, effectively, a pointsource, and the signal strength decreases as the inverse cube of thedistance from the source. A SQUID sensor 32 placed ipsilateral to asulcus-generated signal has characteristics of a dipole such that thesignal strength decreases as the inverse square of the distance from thesource. The SQUID sensors 32 contralateral to the gyms 64 of interestdemonstrate a decay in intensity as the cube function of distance. Inthis configuration, the output is thus markedly simplified forinterpretation but not degraded.

The measurement setup may also include an MRI device for collection ofMRI data. This MRI data may be used to perform source localizationwithin the brain; however, as described above, source localization maybe estimated without the MRI data, such as when the magnetic signal is awell-known response from a well-known stimulus.

Referring to FIG. 1D, the MEG system 48 includes a sensor head 12 incommunication with a computer 20. The computer 20 includes signalprocessing 112 and a categorization module 114 for determining weightsof the candidate parameters of the model, and the computer 20 storesmodel candidate parameters 116.

II. MEG Signal Measurements

The MEG system 48 described above detects signals from the brain usingone or more SQUID sensors 32 as discussed above. In one series ofembodiments, these signals are captured following an auditory stimulusprovided to a human patient. Generally, the models described herein arebuilt using and can evaluate patients based on providing multipleiterations of an auditory stimulus to the patient. An “epoch”, as usedherein, refers to a single measured response or single output over asingle predetermined period of time, such as with respect to a singlestimulus event. As a specific example, to build an Alzheimer's DiseaseDetection (“ADD”) or Cognitive Impairment (CI) model or evaluate anygiven patient with respect to the ADD model or CI model, generallymultiple epochs are collected. In the experimental Example described inSection IV below the number of epochs collected was approximately 250,however this may vary by implementation.

The frequency of auditory stimulus, duration of stimulus, and pattern ofstimulus may vary by implementation. For example, the patients whocontributed MEG data for the generation of the example models in SectionIV below were presented with a series of 700 Hz standard tones of 50msec duration, spaced every 2500 msec. With a proportion of 1 to 5, adeviant tone (600 Hz) was randomly presented. All tones were presentedto the test patient's left ear, for a total of 250 samples. Testpatients were scanned in three different runs, with two of those runsbeing performed during the same visit. In one embodiment, only theresponses to standard tones were analyzed, and responses to devianttones were discarded.

Although specific tone frequencies, tone durations, inter-trialintervals, and numbers of epochs were used to collect the MEG datadescribed herein, it will be appreciated that a range of values may beselected for each. The tone frequencies may be in the range of 500 to1000 Hz or alternatively in the range of 600 to 700 Hz. The toneduration may be in the range of 25 to 75 msec. The inter-trial intervalsmay be at least 500 msec or alternatively in the range of 500 to 3000msec. The total number of epoch collected in a single session may be atleast 200 or alternatively at least 250.

The measurement setup and computer 20 particularly may map the magneticfield strength to the surface of the cerebral cortex. The array of SQUIDsensors 32 are located over the cortical region controlling the functionto be inventoried. For auditory evoked potential, the sensor heads 12are placed over the superior temporal gyms to record initial response toa repeated sound stimulus. The patient support device 14 may be moved torefine the topological image quality. The contour maps of magnetic fieldintensity may be collected over a 500-600 msec epoch after a definedstimulus (e.g., pitch, intensity, duration, and repetition). To achieveadequate data homogenization in order to render the content of thecollected MEG data understandable without degrading it, the datacollection may be limited to neural transmission originating in the mostsuperficial neurons lining the sulci of the relevant gyms of the humancortex. These processes were carried out with respect MEG data thatserved as the basis for the generation of the example models of SectionIV below. The output may be presented as a contour map with no attemptbeing made to determine the underlying dipole or current structure.

Data collected from the MEG system that is passed to the computer 20 maybe band-pass filtered, for example by retaining frequencies in the rangeof 1-30 Hz and removing frequencies outside that range. This helps keepmost of the variance in the power of the recordings and also to removeany slow drifts in the data, normally related to recording artifacts.The data may also be otherwise processed, one example of which issegmenting an incoming data stream into separate epochs by time. Forexample, the computer 20 may determine the timing of the presentation ofeach standard tone, and data in the 100 msec preceding the presentation,and 500 msec after, may be recorded and averaged over all presentations.This procedure results in one time series per channel, containing 600samples from −100 msec to 500 msec, where time zero determined thepresentation of the standard tone. These processes were carried out withrespect to MEG data that served as the basis for the generation of theexample models of Section IV below. In one example scenario used tobuild the test ADD model described in Section IV below, the number ofaveraged presentations was between 207 and 224, depending on patientsand runs.

Other types of signal processing may also be performed. For example,data collected by the Elekta Neuromag® 306 channel system may be furtherprocessed using Elekta Neuromag's Maxfilter™ software to remove sourcesof outside noise. This signal processing was carried out with respectMEG data that served as the basis for the generation of the examplemodels of Section IV below. Depending upon the physical setting of datacollection and specific data collection tools used, additional or evenfewer signal processing steps than described herein may be helpful aswell, particularly due to variation based on the physical location ofthe recording (e.g. the amount of external noise in the site). Thus,signal processing may not be necessary based on the recording instrumentand site used in future applications of this description.

FIG. 2A illustrates the averaged response of a signal (a “signalillustration”) to the standard tone for each SQUID sensor 32, bothgradiometers and magnetometers, with each signal illustration beingarranged in a location in FIG. 2A corresponding to the relative locationof the SQUID sensor 32 in the array in the sensor head 12, according toone embodiment. Each signal illustration in FIG. 2A represents one ofthe 306 sensors (not separately labeled), where the horizontal axis goesfrom −100 to 500 msec, where 0 represents the time at which the tone waspresented to the patient. As discussed above, the Y axis value forsignal received from the SQUID sensor 32 is a quantification of magneticactivity measured in a particular part of the brain, as indicated bymagnetic fields detected by the SQUID sensors 32.

Zooming in on an example SQUID sensor's response provides a prototypicalwaveform pattern such as shown in FIG. 2B, which shows an example of anaveraged evoked stimulus response in an area of interest in the brain asmeasured by a single SQUID sensor 32 of the sensor head 12. The positiveand negative sensor magnitude depends on the position of the sensor andare therefore arbitrary, but peak B 92 is shown and described as anegative peak throughout the present disclosure for consistency. Theexample waveform pattern of FIG. 2B was collected from a test patientwith no measured cognitive dysfunction.

The human brain's response to the auditory stimulus, on average and forparticularly placed SQUID sensors 32, includes several curves that peak,that is they have values of zero for their first derivative at somepoint after stimulus. These peaks include a peak A 90 defining a firstlocal maximum 80, followed by a peak B 91 defining a local minimum 81,followed by a peak C 92 defining a second local maximum 82, followed bya return to a baseline. Peak A 90 is commonly known in the EEGliterature as “P50” or “m50”. Peak B 91 is commonly known in the EEGliterature as “N100”, “m100”, or an awareness related negativity (“ARN”)peak. Peak C 92 is commonly known in the electromagnetic literature as“P200”. On average, the first local maximum 80 is generally observedwithin about 50 to 100 msec after the stimulus, which was presented attime zero in FIG. 2B. The local minimum 81 is generally observed betweenabout 100 and 150 msec after the stimulation. The second local maximum82 is generally observed between about 200 and 400 msec after thestimulation event.

Throughout the remainder of this description and in the claims, it issometimes useful to refer to these peaks without reference to whichspecific peak is intended. For this purpose, the terms “first peak”,“second peak”, and “third peak” are used. Where the “first peak” iseither peak A 90, peak B 91, or peak C 92, the “second peak” is adifferent one of the peaks from the “first peak”, and the “third peak”is the remaining peak different from the “first peak” and the “secondpeak”. For example, the “first peak” may be arbitrarily associated withpeak B 91 for this example, with the “second peak” being peak A 90 andthe “third peak” being peak C 92, and so on.

III. Model Development

Once MEG signals have been collected from a set of test patients 50 asmodel MEG data, possible candidate parameters of the model MEG data maybe identified, analyzed, and selected to determine the model parametersthat will make up the ADD model. The heat maps introduced in SectionIII.B. provide one way in which the MEG data may be analyzed for use inperforming these tasks.

III.A. Sensor Selection

In developing the ADD model, consideration is given to specific signalsin the sensor head 12 that are used to train and use the model. Forexample, for models in Section IV (except for Section IV.E) below, apool of channels of SQUID sensors 32 located ipsilaterally to the tonepresentation, where the most discriminating parameters between the twogroups were initially identified, were reviewed. Within that channelpool, in one implementation the channel with the least variability inthe latency of peak A 90 was chosen. Specifically, the latency of peak A90 (e.g., the time point from stimulus presentation to maximaldeflection within the expected peak A 90 timeframe) was calculated forthe data from each of a group of channels previously identified tocapture the ipsilateral response. That process was repeated two thousandtimes, sampling the epochs with replacement (bootstrap) in eachiteration. This procedure yielded a distribution of latencies of peak A90 for each channel in the pool, and the channel with smallestvariability in the latency of peak A 90 was selected.

In other implementations, other or additional factors may be used toidentify one or more channels whose test data will be analyzed to buildthe ADD model. Examples of these factors and example models built usingthese factors are discussed in Section IV.E below.

In other models, other criteria may be used to select one or more SQUIDsensors 32 whose test data will be analyzed to build the ADD model, suchas, for example, the best match to the expected 3-peak pattern (peak A90, peak B 91, and peak C 92) or the strongest peak B 91 when respondingto auditory tones.

III.B. Candidate Parameter Identification

There is a great deal of information that can be obtained from therecorded epochs of MEG signal data. On an individual epoch level orafter averaging many epochs, the following pieces of information may bedetermined for use as candidate parameters themselves, or as precursorinformation towards the determination of other candidate parameters. Thecomputer 20 may determine maximum 80 (or maximum “strength”) of peak A90, the maximum 81 of peak B 91, and the maximum 82 of peak C 92, ineither absolute units of magnetic field strength, electrical activity,in some other units, or on a relative scale such as % of largestrecorded epoch for that patient or relative to some baseline. Thecomputer 20 may also determine an associated time of occurrence of eachpeak after stimulation, which are referred to hereafter as latency A,latency B, and latency C, respectively. Latencies may also be computedin other forms, for example the latency of peak B 91 may be calculatedrelative to the average peak A 90 latency, for that patient or for apopulation, and so on. The computer 20 may also determine an area underthe curve with respect to a baseline, relative to that patient orrelative to a population, for peak A 90, peak B 91, and peak C 92. Theonset and offset of each peak 90, 91, 92, calculated, for example, asmean (baseline)+/−2 standard deviations, may also be useful in candidateparameter identification.

Due to the variation across epochs, valuable additional information maybe obtained by analyzing the MEG data in heat maps. Visualizing this MEGdata in the form of a heat map, such as the one shown in FIG. 3A, allowsvisual inspection of the set of raw epoch data to identify trends andparameters that are hidden or lost in averaged or otherwise collapsed orconflated MEG data. In such a heat map, each of the responses, orepochs, is plotted as a horizontal line with a color scale representingthe strength of the measured magnetic field. These heat maps allowvisual interpretation of the set of raw epoch data that the computer 20processes in generating and using the ADD model. Although forconvenience some of the following descriptions of the generation and useof the ADD model are described with respect to calculations that may beperformed with respect to and on the data in these heat maps, those ofskill in the art will appreciate that in practice the computer 20performs calculations with respect to the data itself, without regard tohow it would be visualized in a heat map.

Many candidate parameters were identified by observation of an apparentcorrelation between the candidate parameter and the Mini-Mental StateExamination (“MMSE”) score of the test patient. The apparentcorrelations were mostly initially identified by visual inspection ofthe heat maps of model MEG data. For example, it was observed that theAD test patients (i.e., test patients with lower MMSE scores) tended tohave more epochs with peak A 90 than normal test patients 50. It wasalso observed that normal test patients (i.e., with higher MMSE scores)tended to have more epochs with all three peaks. The weaker peak A 90half of the epochs that have peak A 90 were observed to have a higheramplitude of peak B 91 in normal test patients than AD test patients.Finally, the number of epochs with peak C 92 in the weaker peak A 90half of the epochs that have peak A 90 were observed to be within anintermediate range for normal test patients.

FIG. 3A through FIG. 3C illustrate several example heat maps, withepochs on the y-axis and time with respect to the stimulus time on thex-axis. Each heat map represents one complete auditory stimulation testrun for one patient. Each epoch represents a response to a singlestimulus. In these heat maps, white refers to a neutral (close tobaseline) magnetic or electrical field as measured by one of the SQUIDsensors 32, while red arbitrarily refers to a positive magnetic orelectrical field and blue arbitrarily refers to a negative magnetic orelectrical field. For each epoch, the color scale is normalized fromblue to red based on the data in the epoch. The relative intensity ofthe positive or negative field is indicated by the intensity of the redor blue color, respectively. The epochs in the heat maps of FIG. 3A,FIG. 3B, and FIG. 3C are not ordered chronologically but rather by asimilarity metric of the signal within the window of peak B 91. Any oneof a number of different sorting metrics may be used. For example, theepochs in the heat map may be sorted based on the duration of one of thethree peaks 90, 91, 92, the maximum of one of the three peaks 90, 91,92, or the latency of one of the three peaks 90, 91, 92. After thesorting of all epochs is done, for visual representation the highestpeak B 91 is placed at the bottom in FIG. 3A through FIG. 3C.

FIG. 3A shows a heat map of the MEG data from a normal patient. Peak B91, represented in blue between about 90 and 200 msec, has a uniform,well-defined onset and leads to a strong peak C 92, represented in redand appearing after peak B 91. In contrast, FIG. 3B shows the MEG datafor an AD patient having a peak B 91 with a less-uniform, less-definedonset. In this case, the peak B 91 is not particularly strong, andalthough the peak C 92 is not very uniform or well-defined, it is stillclearly present. Not all AD patient MEG data, however, showed this sametype of deviation. The MEG data (not shown) from one AD patient shows astronger peak B 91 with a less-uniform, less-defined onset and a peak C92 that is barely noticeable. MEG data (not shown) for two other ADpatients shows a much stronger peak A 90 than for the MEG data of thenormal patient shown in FIG. 3A. The onset of the peak B 91 was fairlyuniform and well-defined for those AD patients but was delayed incomparison to peak B 91 of the normal patient, and peak C 92 was visiblebut weak. Finally, FIG. 3C shows MEG data for another normal patient,but the data is very atypical in comparison to the observed MEG data ofthe other normal patients. Peak A 90, peak B 91, and peak C 92 arefairly weak and poorly-defined in the MEG data in FIG. 3C, with peak B91 starting later and ending earlier than for other normal patients.Collectively, these heat maps illustrate that reliance on averaged orotherwise aggregated epoch data alone obscures the variety in stimulusresponses that will occur in actual patients, and thus is likely toalone be insufficient to generate a model for discriminating betweennormal and AD patients.

At least some of the candidate parameters for the ADD model wereidentified or are more easily explained by looking at the non-averagedepochs of MEG data organized in heat maps. Some of these candidateparameters include a percentage of epochs having a particular peak orcombination of peaks. The determination of whether or not a given epochhas a given peak can be based on any one of a number of calculations,examples of which are described further in the following subsections ofSection IV.

Additional candidate parameters include identified subsets of epochs ina given set of scans from a single session for a given SQUID sensor.Specifically, two (or more) subsets may be identified for a given testpatient dividing the epochs based on any one of the candidate parametersor some other aspects. For example, two subsets may be identified, basedon a candidate parameter such as presence of one of the peaks wherepresence is a relative measure of magnetic field strength relative tothe other epochs for that test patient. In this example, the subset withthe peak being present may be divided into two further subsets of a“stronger” subset including some threshold proportion of the epochs(e.g., 50%) with the higher (or stronger, or strongest) relativepresence of the peak, and also of a “weaker” subset including theremaining proportion of the epochs with the lower (or weaker, orweakest) relative presence of peak (or absence thereof). Other candidateparameters or aspects of the epoch data may also be used to generatesubsets, such as strong and weak subsets, including, for example, peaktiming and variability, and peak amplitude and variability.

Yet additional candidate parameters may be determined based on thoseidentified subsets. For example, any given candidate parameter mentionedin Section IV may be determined with respect to an identified subset ofepochs. For example, if a strong peak A 90 subset is identified, whichmay represent 50% of the epochs in the set of scans from a singlesession of a patient having the strongest relative presence of peak A 90compared to a weak peak A 90 subset, another candidate parameter may bethe mean or median amplitude (in terms of magnetic field strength) ofthe peak B 91 in the strong subset. One of skill in the art willappreciate the wide variety of possible candidate parameters that maypossibly be generated by dividing the epoch data from the set of scansfrom a single session of a patient and sensor according to oneaspect/candidate parameter, and then calculating another candidateparameter based on an identified subset.

III.B.1. Candidate Timing Parameters

Some of the candidate parameters may be generally categorized as peaktiming parameters, including peak latency parameters, peak onsetparameters, peak offset parameters, and peak duration parameters. Eachof these candidate parameters may be calculated for each of peak A 90,peak B 91, and peak C 92. For these candidate parameters, the values ofthe candidate parameters for the ADD model are determined based onepochs from test patient training data that are determined to includeall three peaks 90, 91, 92, herein referred to as the tri-peak subset.Thus, instead of using all epochs from the scan session of a testpatient 50 of a SQUID sensor 32 to calculate the value of the timingparameter for each peak, it was first determined which epochs had eachpeak, and then the value for the timing parameter for each peak wascalculated. The average and variability of the value of each timingparameter was calculated through bootstrapping, and these averages andvariabilities are additional possible ADD model candidate parameters.Additional parameters may also include the values of the timingparameters (and their averages and variabilities) as instead calculatedfrom averaged response MEG data (i.e., the average of all epochstogether per SQUID sensor per patient).

More specifically, the latency of peak B 91 may be estimated as a timepoint in each epoch at which the signal displayed its maximum absolutevalue. The values of the peak B 91 latency average [“latencyB (mean)”]and variability [“latencyB (var)”] candidate parameters for a particularmodel patient may be calculated based on the data set of the individualpeak B 91 latency points for the epochs under consideration (e.g., thosehaving all three peaks) for that particular model patient in thetraining set. The resulting candidate parameter values may then be fedinto the ADD model for training.

The latency of peak A 90 may be estimated based on the time point ineach epoch at which the first time derivative of the signal became zero,counting backwards from the latency of peak B 91. The values of the peakA 90 latency average [“latencyA (mean)”] and variability [“latencyA(var)”] candidate parameters may be determined based on the time pointsfor these epochs under consideration for each patient in the trainingset.

Again, starting at the latency of peak B 91 and going backwards, theonset of peak B 91 may be estimated based on the time point in eachepoch at which the absolute value of the signal became more than twicethe standard deviation of the baseline signal (for time <0). The valuesof the peak B 91 onset average [“onsetB (mean)”] and variability[“onsetB (var)”] candidate parameters may be determined based on thetime points for these epochs under consideration for each patient in thetraining set.

Similar to the onset of peak B 91, the time point in each epoch for theoffset of peak B 91 may be estimated using the same criteria butcounting forward from the latency of peak B 91. The values of the peak B91 offset average [“offsetB (mean)”] and variability [“offsetB (var)”]candidate parameters may be determined based on these time points forthe epochs under consideration for each patient in the training set.

Starting at the latency of peak A 90 and going backwards in time, theonset of peak A 90 may be estimated as the time point in each epoch atwhich the first time derivative of the signal changes sign. The valuesof the peak A 90 onset average [“onsetA (mean)”] and variability[“onsetA (var)”] candidate parameters may be determined based on thesetime points for the epochs under consideration for each patient in thetraining set. Note that the onset of peak B 91, as defined herein, maybe the same as the offset of peak A 90. Similarly, the offset of peak B91, as defined herein, may be the same as the onset of peak C 92.

The offset of peak C 92 was calculated as the first time point in eachepoch when the signal returns to the same value as in the offset of peakB 91, or some threshold time (e.g., 450 msec post stimulation),whichever occurs sooner. The value of the peak C 92 offset average[“offsetC (mean)”] and variability [“offsetC (var)”] candidateparameters may be determined based on these time points for the epochsunder consideration for each patient in the training set.

The duration of peak B 91 in each epoch is the offset of peak B 91 minusthe onset of peak B 91. The values of the peak B 91 duration average[“duration (mean)”] and variability [“duration (var)”] candidateparameters may be determined based on these time points for the epochsunder consideration for each patient in the training set.

For each of these timing parameters, a particular process forcalculating the value of the candidate parameter is provided above,however those of skill in the art will appreciate alternative mechanismsof calculating these quantities may be established.

III.B.2. Candidate Subset Parameters

The determinations of the values of other candidate parameters for thetest patients in the training set involves further processing of theepochs of the MEG data. As above, illustration by heat map is useful inconceptualizing these candidate parameters. One type of processingincludes determining which epochs include one or more of the peaks. Thiscalculation can be used for determining a number of candidateparameters, including those based on strong/weak subsets of epoch asintroduced in Subsection IV.B above.

In one embodiment, to perform this processing and/or identify candidateparameters, the epochs in the heat map are sorted based on similaritywithin specific time windows. Often, though not necessarily, the sortingis with respect to a particular “sorting” peak. For example, the epochsin FIG. 3A may be sorted based on the time window of sorting peak B 91,such that epochs at the bottom of the plot look more similar, and aremore likely to have a peak B 91, than epochs at the top. To do thesorting, initial peak boundaries are first estimated using all epochsfor a test patient, and those initial estimates are used to sort theheat map and count the epochs that displayed each peak. In oneembodiment, sorting is performed using spectral embedding thattransforms the data to a single dimension, after applying a radial basisfunction (“RBF”) kernel with a gamma value such as gamma=0.1.

After the epochs are sorted based on their similarity within a timewindow related to peak A 90, peak B 91, or peak C 92, a cutoff epoch fordelineating between which epochs are determined to have and to not havethe sorting peak is selected that maximizes the correlation of thesorted area within the time window. In one embodiment, an ideal linearsignal decay function is used to determine the maximum of thecorrelation within the time window. For example, assume peak A 90 is thesorting peak and there are a total of 200 epochs. When visuallyexamining the heat map sorted in the initial guess for peak A 90, onlyabout the bottom 30% of the epochs had peak A 90 in one case.Computationally, to determine the cutoff epoch, the computer 20 maycreate 200 different images where the signal in the time window for peakA 90 linearly decays from the “bottom” of the heat map to one of the 200epochs, and remains zero after it ends its decay. The image that has thehighest correlation with the actual heat map is considered the imagewhere the zero is around the 30% mark.

FIG. 3D schematically shows the determination of the nB value for asample set of scans from a single session. The real heat map 70 isspatially correlated with every possible ideal heat map 72 from noepochs having peak B 91 up to all of the epochs having peak B 91. Eachepoch is assigned a normalized maximum value based on the maximum valueof the strongest peak B 91. For a given sample set, the peak latencies,onsets, and offsets are determined using bootstrapping. Those threetiming variables are then used in determining nB (or nA or nC). Thesorting of the heat map is done using only the data within theonset-to-offset time window of the peak being analyzed. After nB (or nAor nC) is determined, all of the epochs from 1 to nB (or nA or nC) areclassified as having peak B 91 (or peak A 90 or peak C 92).

The ideal heat maps 72 for nB=30, nB=50, nB=110, and nB=190 are shown inFIG. 3D for the real heat map 70 having about 200 epochs. Each idealheat map 72 has a linear gradient within the peak B 91 window, whereepoch one has a value of one (e.g., dark blue) and epoch nB has a valueof zero (e.g., white). The nB value for the ideal heat map 72 with thehighest correlation to the real heat map 70 is assigned as the nB valuefor the real heat map 70. A similar approach is used to assign thevalues for nA and nC.

Using this approach, it can be determined which specific epochs have (orlack) each of the three peaks 90, 91, 92, and the number of epochs witheach peak can be calculated, as well as how many epochs have everypossible combination of the three peaks 90, 91, 92. Said differently,the tri-peak subset of epochs can be determined. Additionally, thevalues for a number of the candidate parameters for each patient in thetraining set can be determined, including the candidate parameterregarding the number of epochs with peak A 90 [nA], the candidateparameter regarding the number of epochs with peak B 91 [nB], thecandidate parameter regarding the number of epochs with peak C 92 [nC],the candidate parameter regarding the number of epochs with peak A 90and peak B 91 [A*B], the candidate parameter regarding the number ofepochs with peak A 90 and peak C 92 [A*C], the candidate parameterregarding the number of epochs with peak B 91 and peak C 92 [B*C], andthe candidate parameter regarding the number of epochs with peak A 90,peak B 91, and peak C 92 [A*B*C]. The values for these candidateparameters may be determined as a number count, or as a fraction of thetotal number of epochs for that test patient.

The values of other candidate parameters may also be determined for eachtest patient 50 in the training set. The values of the area of peak A 90[areaA], area of peak B 91 [areaB], and the area of peak C 92 [areaC]candidate parameters are simply the aggregated magnitude, that is theamount of area that is blue (i.e., with positive magnetic field signal)for peak A 90 and peak C 92, respectively, and red (i.e., with negativemagnetic field signal) for peak B 91 in the epochs that have beendetected to contain peak A 90, peak B 91, and peak C 92, respectively.The value of an area ratio candidate parameter (e.g., [areaA/areaC],[areaA/areaB], [areaB/areaC] or any inverse thereof) is simply the ratioof these two numbers.

The values of other candidate parameters may be determined by creatingstrong and weak subsets, as introduced above. The value of the candidateparameter for the strong peak A 90 epochs containing peak B 91 is basedon the number of epochs having a peak B 91 in the strong peak A 90subset (e.g., half/50% cutoff) of epochs [“strongA_Bnum”]. Similarly thevalue of the candidate parameter for the weak peak A 90 epochscontaining peak B 91 is based on the number of epochs having a peak B 91in the weak peak A 90 subset [“weakA_Bnum”]. The value of the candidateparameter for the amplitude of peak B 91 in the strong peak A 90 epochsis based on the average amplitude (e.g., amount of red) of peak B 91 inthe epochs in the strong peak A 90 [“strongA_Bamp”] subset. The value ofthe candidate parameter for the amplitude of peak B 91 in the weak peakA 90 epochs are based on the average amplitude (e.g., amount of red) ofpeak B 91 in the epochs in the weak peak A 90 [“weakA_Bamp”] subset. Inother embodiments, these candidate parameters measuring amplitude may bebased on another factor other than average, such as median andgenerally, any measure of amplitude may be used.

Values for other similar candidate parameters may also be calculated forthe reverse situation of subsets including peak B 91, with values basedon peak A 90 amplitude or number [“strongB_Anum”, “weakB_Anum”,“strongB_Aamp”, “weakB_Aamp”]. Further values for candidate parametersmay also be calculated based on any permutation of a given subset ofepochs (e.g., strong or weak) containing a peak (e.g., A, B, or C), andsome measure of a quantity of the epochs in that subset (e.g., amplitudeor count of another one of peak A 90, peak B 91, or peak C 92).

III.B.3. Other Candidate Parameters

The feature ratio area under the curve [“rAUC”] is calculated as theratio of the area under the curve (“AUC”) of peak C 92 to the AUC ofpeak A 90 from the averaged MEG data. The boundaries of peaks A and Care defined manually for each run, based on when each peak started andfinished with respect to the horizontal baseline. Boundaries arestraight vertical lines crossing the time chosen for the beginning andend of each peak. The area is then calculated by creating a straightbaseline from the starting point of the boundary to the ending point ofthe boundary and summing the magnitude of the signal with respect tothis baseline. Finally, the ratio between the two areas under the curvesis calculated. In exemplary experiments, rAUC tended to be greater innormal test patients than cognitively-impaired test patients.

For the ratio latency [“rLat”], the latency of each peak from theaveraged MEG data is determined by finding the time of the highestabsolute magnitude of the signal within the three sets of pre-determinedboundaries. Then, the difference between the latency of peak C 92 andlatency of peak B 91 is calculated, and similarly, the differencebetween latency of peak B 91 and latency of peak A 90. The ratio ofthese differences is the value for rLat. In exemplary experiments, rLattended to be lower for the cognitively-impaired test patients and wasparticularly low for one such test patients.

After an initial identification of the rAUC and rLat candidateparameters and investigation of their potential as model parameters, amore thorough identification and investigation was performed. Asdiscussed previously, this included not just looking at averaged MEGdata from numerous scans but also investigating the distribution of theactivation over epochs in the heat maps of the model MEG data.

Other candidate parameters based on evaluating the heat maps included[“areaA_ratio”], which is the ratio of the area of peak A 90 in the weakpeak A 90 epochs to the area of peak A 90 in the strong peak A 90epochs; [“Bamp_ratio”], which is the ratio of the overall amplitude ofpeak B 91 in the stronger half of peak A 90 epochs to the overallamplitude of peak B 91 in the weaker half of peak A 90 epochs (a similarparameter can be determined and used for the C peaks [“Camp_ratio”], andsimilarly for any permutation of the peaks used to determine the weakand strong subsets, and the peak used to determine the ratio); [“BnumsA/wA”], which is the ratio of the number of epochs having peak B 91 inthe stronger half of peak A 90 epochs to the number of epochs havingpeak B 91 in the weaker half of peak A 90 epochs; [“Camp_ratio”], whichis the ratio of the overall amplitude of peak C 92 in the stronger halfof peak A 90 epochs to the overall amplitude of peak C 92 in the weakerhalf of peak A 90 epochs (a similar parameter can be used for the B peak[“Bamp_ratio”], and similarly for any permutation of the peaks used todetermine the weak and strong subsets, and the peak used to determinethe ratio); and [“Cnum sA/wA”], which is the ratio of the number ofepochs having peak C 92 in the stronger half of peak A 90 epochs to thenumber of epochs having peak C 92 in the weaker half of peak A 90epochs. Generally, further permutations of the above parameters are alsopossible. For example, any parameter including a ratio can also becalculated by inverting the values described above as making up theratio.

Another candidate parameter, [badInPool], that can be added is asummation of how many candidate parameters in the pool were outside therange for normal test patients. For example, if the pool includes 17candidate parameters, the value of [badInPool] is in the range of 0 to17, depending on how many of the 17 candidate parameters a given AD testpatient has a value outside the Gaussian distribution fitted to thenormal test patient values. In other words, for each of the 17 candidateparameters, the normal values are gathered and fit to a Gaussiandistribution. For each candidate parameter, if the value of thecandidate parameter for an AD test patient has a probability of being inthat distribution that is smaller than the smallest normal test patientprobability, then a value of one is added to the [badInPool] candidateparameter. In other words, the less likely the excluded AD test patientwas to be part of the normal distribution, the higher the value of the[badInPool] parameter.

To determine the [badInPool] candidate parameter, a separate calculationis made for each of the candidate parameters already in the ADD model.For a given candidate parameter, the MEG data for all normal testpatients according to an already-determined cutoff for that modelparameter (based on whether the MEG data comes from a normal testpatient) is fit to a distribution, such as a normal (Gaussian)distribution. That distribution is used to estimate the smallestprobability among normal test patients to be part of the normal testpatients, where that value is used as a cutoff to mark the value of agiven parameter as “bad” or not. In a leave-one-out cross-validationframework, the left-out patient is not used when estimating the normaldistribution (although if the left-out patient were an AD patient, thevalue would not be used anyway).

The value of the [badInPool] candidate parameter for each patient is asimple summation of how many other candidate parameters for that testpatient had smaller probabilities of being in the distribution fornormal test patients than the smallest normal test patient probability.In an example ADD model having six other candidate parameters aside from[badInPool], [badInPool] can go from 0 to 6.

Another possible, similar candidate parameter is [weightInPool], whichis a more detailed version of [badInPool]. The weight for [weightInPool]is a summation of the absolute differences between the smallest normaltest patient probabilities and that test patient's correspondingprobability of being in the distribution for normal test patients,summed over the set of candidate parameters in the model (other than[badInPool]). [badInPool] and [weightInPool] are both posthocparameters.

III.C. Model Parameter Selection

The candidate parameters were evaluated based on whether they werereproducible within and across test patient visits (each visitgenerating a set of epochs) for reliability and stability, respectively.Bland-Altman plots were used to measure those characteristics. Two suchplots appear in FIG. 3E and FIG. 3F, where the triangles are associatedwith MEG data from normal test patient and the circles are associatedwith MEG data from AD test patients. FIG. 3E shows a Bland-Altman plotof the reliability of the A*B*C candidate parameter. FIG. 3F shows anexample Bland-Altman plot of the stability of the A*B*C candidateparameter for a set of test patients. In short, these plots compare themean of two measurements and their standard deviation. The horizontallines in FIG. 3E and FIG. 3F are 95% confidence interval lines, and anycandidate parameter that had more than one patient outside theconfidence boundaries for the reliability or the stability was deemedunsatisfactory.

In other embodiments, other criteria and methods may be used to evaluatethe reliability and stability of candidate parameters, including, butnot limited to, intraclass correlation coefficient (“ICC”) andregression analysis.

Among the wide variety of possible candidate parameters that may be usedto build the ADD model, thirty-seven candidate parameters wereidentified from visual analysis of MEG data to build one implementationof an ADD model. The subtle differences between the MEG scans of AD testpatients and “normal” test patients described above were identified bycareful manual visual review and observation and not by a computeralgorithm. The 37 candidate parameters, previously described in SectionIII.B, include (as ordered from best to worst in terms of excluding ADtest patients from the distribution for normal test patients) asweakA_Bamp, strongA_Bnum, nA, weakA_Camp, A*B*C, strongA_Bamp, B*C,areaC, duration (var), Cnum sA/wA, areaA, A*C, weakA_Cnum, nC,areaA_ratios, latencyA (var), onsetA (var), A*B, nB, offsetB (mean),strongA_Cnum, offsetB (var), Bnum sA/wA, Bamp_ratio, areaA/areaC,latencyB (mean), areaA/areaC, latencyB (var), offsetC (var), latencyA(mean), Camp_ratio, onsetA (mean), onsetB (mean), onsetB (var), duration(mean), strongA_Camp, and offsetC (mean).

Some of these candidate parameters were selected for further analysisbased on being reliable and stable candidate parameters. Furtheranalysis included determining the correlation between the candidateparameter and the MMSE score of the test patient 50. The selection ofwhich reliable and stable candidate parameters became model parameterswas based, at least in part, on the weights the linear and non-linearmodels assigned to the model parameters.

It is important to note that two patients with very similar MMSE scoreswere found to have very different peak C 92 amplitudes, which highlightshow these candidate parameters may offer new insights into the diseasethat were hidden by just looking at MMSE scores.

III.D. Model Training

Once ADD model parameters are selected, the ADD model is trained toclassify patients based on their MEG data. A wide variety of machinelearning techniques can be used to create the ADD model, examples ofwhich include Random Forest Classifiers (“RFC”), Random ClassifierRegressors, Gradient Boosting, Support Vectors (also known as SupportVector Machine or “SVM”), Linear SVM, Radial basis function kernel SVM(“RBF SVM”), Linear Regression, Logistic Regression, and other forms ofregressions. This list is not exhaustive, and one of skill in the artwill appreciate that other machine learning techniques may also be used,including techniques in the field of deep learning such as NeuralNetworks.

Generally, training these models generates a set of coefficients, whichmay also be referred to as weights, that represent directly orindirectly how the values for the various model parameters correspond toeither an MMSE score or a classification of a disease. In oneimplementation of any of the example models described in Section IVbelow, a set of model test patients were selected to include a subsethaving no known cognitive dysfunction and a subset showing a range ofseverity of symptoms of cognitive dysfunction, specifically cognitivedysfunction associated with AD. However, in practice the principlesdescribed herein may also be applicable to a variety of other diseasesand conditions, including, but not limited to, mild cognitive disorder.In the case of an ADD example model generated using RFC with one-stepclassification, the coefficients may also be referred to as “criticalvalues”, as used in the literature regarding RFC models, in this casefor categorizing the values of particular model parameters for a givenpatient as being normal or AD-indicative.

What the model is trained to detect may vary by implementation. Examplesinclude a two-step classification and a one-step classification. In atwo-step classification, a first model is used to predict the MMSE for apatient, and then a second model is used to categorize or quantify apatient with respect to a disease based on the predicted MMSE score. Ina one-step classification, a single model categorizes or quantifies apatient with respect to the disease directly.

An advantage of the two-step approach is that there is value in the MMSEprediction, as the MMSE encodes several different aspects of humancognition, and is not inherently limited to the normal patient versus ADpatient comparison. As such, other types of two step classifications canbe used, determining MMSE score in a first step and then regressing toevaluate a different cognitive measure such as one of the other diseaseslisted above. A disadvantage of two step classification is that trainingan ADD model to determine an MMSE score requires an assessment of eachtest patient to obtain an MMSE score. In comparison, the one-stepclassification only needs a normal or AD label for training.

For two step classifications, the first step uses a linear/non-linearmodel, generally a linear or non-linear regression, although inalternate implementations more complicated algorithms may be used. Afterthe MMSE score has been predicted, the second step includes using asimple cutoff to classify whether the test patient is a normal testpatient or an AD test patient. For example, a set of predicted MMSEscores of test patients is fit to a linear model and one or more weightsis determined that correlates the predicted MMSE scores with acategorization.

The ADD model may be a static model or a living model. In a staticmodel, the model parameters and their weights are not changed as themodel is used to evaluate and assess new patients. For example, in theRFC example, the normal value limits are calculated by fitting aGaussian distribution to the set of normal patients minus whateverpatient is left out in the cross validation. In a living model, new MEGdata that has been collected from some or all new patients becomesadditional model MEG data used to further train the weights of thecandidate parameters or to add, delete, or change candidate parametersand thereby update the model. For a progressive disease, such as AD, theADD model may also be fine-tuned by monitoring the patients andcollecting model MEG data over time and re-evaluating the earlier ADDmodel MEG data, such as if a particular normal test patient begins toshow symptoms of the progressive disease, to add, delete, or changecandidate parameters and/or retrain the ADD model to re-determine themodel weights, and thereby update the model.

IV. Examples

IV.A. Test Measurement Setup and Example Data Collection Protocol

An Elekta Neuromag® 306 channel MEG system 48 was used to record wholebrain signals. The system had a total of 306 SQUID sensors 32, with eachof the 102 locations having three different SQUID sensors 32: two planargradiometer SQUID sensors 32 and one magnetometer SQUID sensor 32.

FIG. 4A shows the array of SQUID sensors 32 for the Elekta Neuromag® MEGapparatus, with the shaded circles representing the generally mostinformative SQUID sensors 32, out of a pool of gradiometers located onthe ipsilateral side of the helmet, for the ADD models described herein.Each circle in FIG. 4A represents a gradiometer or a magnetometer. Asshown in FIG. 4A, the gradiometer SQUID sensors 32 are paired and aresensitive to magnetic fields that are at 90 degrees to each other. Alsoshown but not labeled in FIG. 4A, a magnetometer SQUID sensor 32 wasassociated with each pair of gradiometer SQUID sensors 32 in the MEGapparatus.

Gradiometer SQUID sensors 32 and magnetometer SQUID sensors 32 arestructurally and functionally different from each other. Magnetometersmeasure the amplitude of the magnetic field (e.g. in Tesla units, T) ata certain point in space. Gradiometers measure the difference orgradient between magnetic fields (e.g. in Tesla/meter units, T/m) in twodifferent points in space. These two points in space may be across thesame spatial plane (e.g., a spatial gradiometer as in the Elekta systemused herein), or along the same (Z) axis (e.g., an axial gradiometer).

The informative gradiometers used to generate the example models in thissection tended to be at the eight locations of SQUID sensors 32 labeledin FIG. 4A, and only the data from these eight SQUID sensors 32 wasused. These eight SQUID sensors 32 are most known for receiving signalsfrom the left temporal region of the brain. These included sensorsMEG0233, MEG0242, MEG0243, MEG1612, MEG1613, MEG1622, and MEG1623 of theElekta Neuromag® 306 channel system. The colors in FIG. 4A represent thefrequency of use in the ADD models described herein. There were a totalof 63 sessions. The frequency of use from top to bottom of the foursensors in the left column was 16, 4, 3, and 9. The frequency of usefrom top to bottom of the four sensors in the right column was 13, 7, 4,and 7. This indicates that a much smaller SQUID sensor head 12 may beused if placed at the proper location on the head of the patient.

The experimental setup discussed above was used to capture the MEG dataused to generate the models in this section. The specific details of thecapture of the MEG data is discussed above in Section II, and is notrepeated here for clarity and to condense this description.

The same set of test patients was used to build the example ADD modelsin this section. The set of test patients included twenty-one testpatients, including ten normal test patients with no indication ofcognitive impairment and eleven test patients who had already beendiagnosed as having AD. An MRI was collected for each subject. Scans torecord auditory evoked fields were run on the test patients inaccordance with the setup and MEG data gathering steps discussed above.MEG recordings were performed in a magnetically-shielded room. All testpatients except for one cognitively-impaired patient also received anMMSE score based on an administered MMSE test. Data from the testpatient without an MMSE score was not used in the regression model butwas used for the one-step classification tasks.

All of the test patients were white except for one black normal testpatient and one black AD test patient. The normal test patient poolincluded five men and five women in an age range of 64 to 84 years, witha median age of 72 and a mean age of 73.9. The AD test patient poolincluded eight men and three women in an age range of 62 to 84 years,with a median age of 78 and a mean age of 76.2.

FIG. 4B shows the three averaged response signal curves 100, 102, 104from three example auditory stimulation test sessions, two done on thesame day and the third being done on a different day, on arepresentative normal patient. These curves illustrate the generalreproducibility between test runs for normal patients. However, theyalso highlight that there is a significant amount of non-uniformitybetween individual epochs even for normal patients, which the exampleADD models described in this section are able to quantify and capture.

FIG. 4C shows the three averaged response signal curves 140, 142, 144from three example auditory stimulation test sessions, two done on thesame day and the third being done on a different day, on arepresentative AD patient. Although two of the curves are very similar,the peaks and valley of the third are significantly greater inmagnitude. These curves illustrate the relative lack of reproducibilitybetween test runs for AD patients. However, like the normal patientcurves they also highlight that there is a significant amount ofnon-uniformity between individual epochs for AD patients as well, againwhich the example ADD models described in this section are able toquantify and capture.

The MEG data used to produce the averaged MEG data curves shown in FIG.2A, FIG. 2B, FIG. 4B, and FIG. 4C may come from hundreds of repetitionsof an evoked response from a single test session. Visualizing this MEGdata in the form of a heat map, such as the one shown in FIG. 3A, allowsvisual inspection of the set of raw epoch data to identify trends andparameters that are hidden or lost in the averaged or otherwisecollapsed MEG data. In such a heat map, each of the responses, orepochs, is plotted as a horizontal line with a color scale representingthe strength of the measured magnetic field.

In developing the example model described in this section, gradiometerSQUID sensors 32 (i.e. only 204 out of the 306 SQUID sensors 32) wereused, since those SQUID sensors 32 had the best power in discriminatingbetween the two groups. These SQUID sensors 32 were selected on thebasis of having minimum variability in peak A 92. For other models,however, the magnetometers (i.e. the other 102 out of the 306 SQUIDsensors 32) may be used in place or in addition to the above-mentioned204 SQUID sensors.

IV.B. Example ADD Model 1

For a first example ADD model, a set of 17 candidate parameters thatwere both reliable and stable (see Section III.C.) were called “good”parameters, which were carried on for future analysis. Although many ofthe candidate parameters that failed the reliability and stability testwere good at discriminating between normal and AD test patients, theywere not selected for this particular ADD model as model parameters,because the candidate parameters were not sufficiently reproducible inother recording sessions of the same test patient.

From the good candidate parameters, normal distributions wereestablished based on the mean and standard deviations of normal testpatient values for each candidate parameter, and the number of AD testpatients having probabilities lower than the lowest normal test patientof being part of the distribution was determined. In other words, thecandidate parameter correctly sorted the AD test patient if the AD testpatient's probability of being in the normal test patient distributionwas smaller than the probability of the least likely normal testpatient. The parameters were then scored based on how many ADs wereoutside the distribution for normal test patients (i.e., how many testpatients were correctly marked as AD patients). That score (i.e. thenumber of AD test patients outside the distribution) was used as apreliminary rank of the good parameters.

The ranked set of 17 good candidate parameters were then selected toidentify the candidate parameters that were included as model parametersin this ADD model. In this example embodiment, the candidate parameterthat marked the most AD test patients correctly was selected first,added to the ADD model and considered the best model parameter. Eachsubsequent model parameter that was selected and added if it added themost information to that previous information (i.e. captured AD testpatients not captured by previous candidate parameters). When two modelparameters marked the same number of AD test patients (or same number ofadditional test patients), both were added together. This procedure wasemployed to minimize the number of candidate parameters used andtherefore reduce the chances of overfitting. The model parameterselection continued until no more AD test patients were left to bemarked.

This procedure selected the following six model parameters: the numberof epochs with all three peaks 90, 91, 92 [“A*B*C”], the number ofepochs with peak A 90 [“nA”], the amplitude of peak C 92 in the weakpeak A 90 epochs [“weakA_Camp”], the amplitude of peak B 91 in the weakpeak A 90 epochs [“weakA_Bamp”], the number of strong peak A 90 epochswith a peak B 91 [“strongA_Bnum”], and the variability of the durationof peak B 91 [“duration (var)”]. To this set of six candidateparameters, the [weightInPool] candidate parameter was also added. Thus,this example ADD model had seven model parameters in total.

The ADD model was then trained using a linear model on those sevenparameters to predict MMSE score. A hard cutoff on predicted MMSE scorewas then used to classify the test patient as normal or AD. No crossvalidation was used, and thus the same data was used for both trainingand testing.

The result of the model was the predicted MMSE score, which was thensplit to classify the data. The model was able to perfectly distinguishbetween normal test patients and AD test patients based on predictedMMSE score.

IV.C. Example ADD Model 2

Another ADD model was built using the same seven candidate parametersfrom the prior example ADD model (example ADD model 1) plus the posthoc[badInPool] candidate parameter for a total of eight model parameters.

Although very good correlation with MMSE score and group separation wasshown in this model, each candidate parameter does not provide an answerin isolation. A very high correlation with MMSE score may be achieved,in one embodiment, by combining the best candidate parameters using anon-linear model (random classifier regressor) to predict MMSE scores,which are used to discriminate between normal test patients and AD testpatients. This work makes it clear that while some test patients aremarked as AD based on many candidate parameters, some others depend oncharacteristics of a smaller set of candidate parameters. This shows howa varied set of candidate parameters is effective at discriminating testpatients. It further shows that candidate parameters derived fromindividual aspects of data from individual epochs are important indiscriminating test patients, rather than, for example, entirely relyingon data that aggregates, collapses or conflates MEG response data frommultiple epochs together, such as by averaging data from multipleepochs.

IV.D. Alternative Modeling Technique ADD Example Models

Different machine learning methods and model designs were tested usingthe full set or a subset of the 17 good candidate parameters describedabove. A summary of these results is shown in Table 1. For each of thesemodel designs/method, both a two-step classification (regression todetermine a hypothetical MMSE score, and then classification as AD ornormal) and a simple classification as AD or normal between the twogroups were tried. The hyperparameters for each machine learning methodwere left at default for each of these models. One of skill in the artwill appreciate that tuning these hyper parameters will generally leadto improvement in the predictive power of these example ADD models.

Table 1 illustrates a number of example ADD models built using differentsets of candidate parameters and trained using different machinelearning techniques. As a key to the following table, “Two-step” and“One-step” denote whether two step classification or one stepclassification was used per the previous paragraphs. The example machinelearning techniques used included Random Forest, Gradient Boosting,Support Vectors (also known as Support Vector Machine or “SVM”), LinearSVM, Radial basis function kernel SVM (“RBF SVM”), a Linear Regression,and a Logistic Regression. All example ADD models in Table 1 weretrained using leave one out cross validation (“LOOCV”).

The sets of model parameters used include “all” (all 17 good candidateparameters) with the [badInPool] and [weightInPool] parameters making 19model parameters total, and all 17 good candidate parameters without the[badInPool] and [weightInPool] parameters making 17 model parameters,labeled in the table as “no InPool.”

In Table 1, “r” denotes the correlation coefficient for all testpatients and accuracy denotes the performance of the model in correctlycategorizing the twenty test patients as normal or AD (e.g., 1 means alltwenty test patients were categorizing correctly, etc.). For all of thetwo-step models, Pearson correlation coefficients (r) and p-value (p),as well as the Spearman correlation coefficient (r) and p-value (p),were calculated separately for both normal (“NV”) and AD test patients.All such values in Table 1 are rounded to two decimal points.

TABLE 1 Machine Learning Method Results NV AD Method r Accuracy PearsonSpearman Pearson Spearman Two-step Random 0.8932 1.0 r = 0.28 r = −0.04r = 0.07 r = 0.11 Forest (all) (p = 0.43) (p = 0.92) (p = 0.84) (p =0.76) Two-step Random 0.6685 0.75 r = 0.77 r = 0.71 r = −0.34 r = −0.40Forest (no InPool) (p = 0.01) (p = 0.02) (p = 0.33) (p = 0.26) Two-stepGradient 0.9091 1.0 r = −0.23 r = −0.03 r = 0.29 r = 0.17 Boosting (all)(p = 0.52) (p = 0.94) (p = 0.42) (p = 0.64) Two-step Gradient 0.36510.65 r = 0.14 r = 0.20 r = −0.18 r = −0.13 Boosting (no InPool) (p =0.70) (p = 0.58) (p = 0.62) (p = 0.73) Two-step Support 0.4435 0.5 r =0.29 r = 0.40 r = −0.34 r = −0.24 Vectors (all) (p = 0.41) (p = 0.25) (p= 0.33) (p = 0.50) Two-step Support 0.2582 0.5 r = 0.17 r = 0.18 r =−0.37 r = −0.33 Vectors (no InPool) (p = 0.64) (p = 0.63) (p = 0.30) (p= 0.35) One-step Linear SVM 0.9047 (all) One-step Linear SVM 0.8571 (noInPool) One-step RBF SVM 0.8571 (all) One-step RBF SVM 0.7143 (noInPool) One-step Logistic 0.9524 Regression (all) One-step Logistic0.8571 Regression (no InPool)

The results of these models illustrate several points. First, the twoposthoc parameters, [badInPool] and [weightInPool] provide a substantialimprovement to a model's performance. The ensemble non-linear models (RFand GBM) tend to outperform the others, given the current set of modelparameters. High classification accuracies may also be obtained withouttaking the intermediate step of predicting MMSE scores. However, forreasons already stated herein, this is a highly useful characteristic ofthe models, for example, for use in evaluating for the presence orprogression of other diseases.

IV.E. Example ADD Models Based on Other Channel Selection CRITERIA

To evaluate the effect of the SQUID sensor 32 selection criterion, otherselection criteria were tested. The tested criteria included selectingthe SQUID sensor 32 that had the highest percentage of epochs havingpeak A 90 (“most peak A”), selecting the SQUID sensor 32 that had thehighest percentage of epochs having peak B 91 (“most peak B”), andselecting the SQUID sensor 32 that had the highest intensity for peak A90 (“highest peak A”) using all epochs.

Once the sensor selection was made, the 37 candidate parameters werecalculated based on the MEG data from those selected SQUID sensors 32,and the stability and reliability of each candidate parameter wasevaluated independently to determine which candidate parameters weregood. The most peak A 90, most peak B 91, and most intense peak A 90sensor selection criteria produced 9, 17, and 11 good candidateparameters, respectively. Example ADD models were then developed using atwo-step classification based on all of the good candidate parameters,and no InPool parameters. Again, RFC was used to predict MMSE scores anda regular cutoff on the predicted value was used to classify as normalor AD for the two-step classification. The results of this evaluationare shown in Table 2.

TABLE 2 ADD Model Results with Alternative Sensor Selection Criteria NVAD Sensor Criterion r Accuracy Pearson Spearman Pearson Spearman Mostpeak A (all) 0.3290 0.6 r = 0.63 r = 0.68 r = −0.27 r = −0.23 (p = 0.05)(p = 0.03) (p = 0.45) (p = 0.52) Most peak A −0.1564 0.45 r = 0.34 r =0.29 r = −0.10 r = −0.09 (no InPool) (p = 0.34) (p = 0.42) (p = 0.79) (p= 0.82) Most peak B (all) 0.5614 0.65 r = 0.26 r = 0.37 r = 0.35 r =0.23 (p = 0.47) (p = 0.30) (p = 0.32) (p = 0.53) Most peak B 0.2784 0.55r = 0.11 r = 0.08 r = 0.48 r = 0.43 (no InPool) (p = 0.77) (p = 0.84) (p= 0.16) (p = 0.21) Highest peak A 0.6105 0.9 r = 0.44 r = 0.35 r = −0.39r = −0.37 intensity (all) (p = 0.21) (p = 0.32) (p = 0.27) (p = 0.29)Highest peak A 0.4665 0.6 r = 0.35 r = 0.42 r = 0.40 r = 0.39 intensity(no InPool) (p = 0.32) (p = 0.23) (p = 0.25) (p = 0.27)

Based on the test data presented herein, none of these alternativesensor criteria provided results as good as using the least variabilityin the latency of peak A 90 as the sensor selection criterion. However,it is clear that other alternative sensor criteria are still predictiveand may be a viable substitute to minimizing peak A 90 latencyvariability. While there are many ways in which a single channel may beselected for use in extracting the features, the characteristic of peakA has yielded the best classifier results so far. That may be because ofactual characteristics of peak A, or the number of stable and reliablefeatures such selection scheme yields, compared to other methods.

IV.F. Additional ADD Model Examples

In order to test how the number of model parameters affects the ADDmodel, a large number of additional example ADD models were created,where the number of good candidate parameters being used was varied forthe Random Forest Regressor (RFR) ADD model in the leave-one-out crossvalidation framework described above in Section IV.E.3. Two-stepclassification was performed: as above, first predicting the MMSE score,second using the MMSE score to classify the patient between normal andAD. As above, the Random Forest Regressor uses its default parameters,and no hyperparameter optimization was performed. Two versions of eachsuch ADD model were created, one with and one without the posthocparameters ([badInPool] and [weightInPool]).

The number of ADD model parameter chosen at random from the pool of 17good candidate parameters was fixed. Then, those model parameters werechosen randomly from the pool of good candidate parameters 200 differenttimes, and histograms were created for the regression coefficient andaccuracy. This produced 16 sets of histogram pairs (i.e., choosing oneparameters at random, all the way to 17). Note that the variability ofchoosing one parameter at random (after 17 iterations), and 17parameters (always the same ones, as there are only 17 parameters),comes from the Random Forest algorithm, which has a random component insplitting the trees.

FIG. 4D shows the average and standard deviation for the Pearson r value(y-axis) as a function of the number of good candidate parameters(x-axis) included in the example ADD models both with and withoutposthoc parameters. In FIG. 4D, the average is illustrated as a solidline, and the standard deviation is illustrated as an envelope aroundthat line.

FIG. 4E shows the average and standard deviation for the classificationaccuracy as a function of the number of good candidate parametersincluded in the example ADD model both with and without posthocparameters. FIG. 4E is otherwise illustrated similarly to FIG. 4D.

FIG. 4D and FIG. 4E show that the more of the good candidate parametersthat are used, the better the performance of the resulting ADD model.They further illustrate that the two posthoc parameters are powerful.Further, the variance between posthoc and no posthoc parametersincreases as the number of model parameters increases. Again, thedeviation when all 17 good candidate parameters are used in the ADDmodel is a result of the randomization component of the Random ForestRegressor.

V. Model Use

A developed model, for example one of the ADD models mentioned abovewith a particular set of candidate parameters, may be applied to other“new” patients who were not part of the training set. The “new” MEG datais collected from the new patients in the same manner as the model MEGdata was collected from the test patients. The new MEG data is thenanalyzed to determine the values of the model parameters for the modelfor the new patient. The values of the new patient's model parametersare compared to the model values for the model parameters, and anassessment of the new patient is provided. The assessment of the newpatient relates to the medical condition that the model was developed toevaluate. The common example throughout this description is fordiscrimination of AD; however the processes throughout this descriptionare applicable to other medical conditions.

The computer 20 calculates the model parameter values from the newpatient MEG data, when possible, but human input may be helpful for thedetermination of some model parameter values, depending on the nature ofthe process to determine the model parameter value. After analysis ofthe new MEG data is complete, the results are provided.

FIG. 5 illustrates an example graphical user interface display that adoctor may use to quickly analyze the new patient after the collectionof the MEG data. The upper left portion of the example display of FIG. 5shows an example heat map of the new MEG data. The lower left portion ofthe example display of FIG. 5 shows curves of averaged MEG data for allepoch, strong peak A 90 epochs, and weak peak A 90 epochs, along withthe estimates for the values of onsets and offsets. The upper rightportion of the example display of FIG. 5 shows an example chart listingthe model parameters of the model, the patient's values for those modelparameters, and the normal values for those model parameters, along withhighlighting of any abnormal results. The lower right portion of theexample display of FIG. 5 shows an example chart that lists othercandidate parameters, the patient's values for those candidateparameters, and normal values, and highlights any abnormal results. Theexample patient in FIG. 5 would be considered to have AD based on theinformation in FIG. 5 .

Regarding the highlighting of abnormal parameters, the individual valuesfor each model parameter contributing to the [badInPool] and[weightInPool] parameters as discussed above in Section III.B.3 can beused as part of a presented graphical user interface (GUI) display todetermine which parameter values to highlight. Generally, when a givenpatient's value for a given model parameter is outside the range that isexpected from a distribution of normal test patients, the value for thatmodel parameter may be marked as abnormal in the GUI. For example, if,as above, the normal test patient values for all test subjects are usedfor model parameter A*B*C, and a distribution (e.g., a normaldistribution) is estimated from that. Assume for this example that thesmallest probability among normal test patients to be in thatdistribution is calculated as 0.2. Consequently any patient withprobability <0.2 of being in the distribution for normal test patientswill have the model parameter A*B*C marked in some distinguishing manner(e.g., in red as presented in FIG. 5 ).

Models that are trained based on the parameters to determine whether apatient is cognitively impaired can be used in methods of diagnosingcognitive impairment in a patient.

Models that are trained based on the parameters to determine whether apatient is cognitively impaired and to discriminate degrees of cognitiveimpairment can be used in methods of staging the extent of cognitiveimpairment in the patient. Such models can also be used in methods ofmonitoring progression of disease. In methods of monitoring diseaseprogression, at least a first determination and a second determinationof the degree of cognitive impairment are obtained at a spaced timeinterval, and the change in degree of cognitive impairment between firstand second determinations is calculated.

Models that are trained based on the parameters to determine whether apatient is cognitively impaired and to discriminate cognitive impairmentcaused by neurodegeneration from cognitive impairment of other etiologycan be used in methods of diagnostically discriminating cognitiveimpairment in a patient caused by neurodegeneration from cognitiveimpairment of other etiology.

The models can also be used in a method of treating a patient havingcognitive impairment, the method comprising administering atherapeutically effective amount of an anti-cognitive impairmenttherapeutic agent to a patient who has been determined through use ofthe model to have cognitive impairment.

In some embodiments, the anti-cognitive impairment therapeutic agent isa disease-modifying anti-neurodegeneration agent. In some embodiments,the anti-cognitive impairment therapeutic agent is a cognitive symptomenhancement agent.

In certain embodiments, the disease-modifying anti-neurodegenerationagent binds to one or more of beta-secretase 1 (BACE-1), gammasecretase, Tau, Aβ, amyloid precursor protein (APP), α-synuclein,leucine rich repeat kinase 2 (LRRK2), parkin, presenilin 1, presenilin2, apolipoprotein E4 (ApoE4), huntingtin, p75 neurotrophin receptor(p75NTR), CD20, prion protein (PrP), and death receptor 6 (DR6).

In specific embodiments, the anti-cognitive impairment therapeutic agentis selected from Table 3.

TABLE 3 Agent (target or mechanism of action) Company ALKS 7119 (CNSmodulator) Alkermes ALZ-801 (amyloid beta-protein inhibitor) AlzheonALZT OP1 (amyloid beta-protein inhibitor) AZTherapies ANAVEX ™ 2-73Anavex Life Sciences ANAVEX ™ Plus (ANAVEX 2-73/donepezil) Anavex LifeSciences apabetalone (RVX-208) (BET protein inhibitor) ResverlogixARC-029 (nilvadipine) Archer Pharmaceuticals ASP3662 (11-beta-HSD1inhibitor) Astellas Pharma US AVN-101 (serotonin 6 receptor antagonist)AllaChem & Avineuro Pharmaceuticals AVN-322 (serotonin 6 receptorantagonist) AllaChem & Avineuro Pharmaceuticals AVP-786(dextromethorphan)analogue/quinidine) Avanir Pharmaceuticals & ConcertPharmaceuticals AVP-923 (dextromethorphan/quinidine) AvanirPharmaceuticals AXS-05 (bupropion/dextromethrophan) Axsome TherapeuticsAZD3293 (BACE inhibitor) AstraZeneca & Eli Lilly azeliragon (TTP488)(RAGE antagonist) vTv Therapeutics BACE inhibitor Eli Lilly BAN2401(humanized anti-amyloid beta mAb) Biogen | Eisai bexarotene(RXR-selective retinoid analogue) ReXceptor BI 409306 (phosphodiesterase9A inhibitor) Boehringer Ingelheim Pharmaceuticals bisnorcymserine(butyrylcholinesterase inhibitor) QR Pharma BPN14770 (type 4 cyclicnucleotide Tetra Discovery Partners phosphodiesterase inhibitor)brexpiprazole (dopamine partial agonist) Lundbeck & OtsukaPharmaceutical bryostatin 1 (protein kinase C stimulant) NeurotropeBioScience CAD106 (beta-amyloid protein inhibitor) GlaxoSmithKline CNP520 (BACE1 protein inhibitor) Amgen & Novartis Pharmaceuticals CPC-201(donepezil/peripherally acting cholinergic Chase Pharmaceuticals blockerfixed- combination)dose) CPC-212 (next-generation acetylcholinesteraseChase Pharmaceuticals inhibitor) crenezumab (beta-amyloid proteininhibitor) Genentech CSP-1103(amyloid beta-protein inhibitor) CereSpirdonepezil transdermal patch Corium International E2027 Eisai E2609(BACE1 protein inhibitor) Biogen & Eisai ELND005 (amyloid beta-proteininhibitor) Transition Therapeutics gantenerumab (amyloid beta-proteininhibitor) Genentech GC021109 (purinoceptor P2Y6 agonist) GliaCureGSK933776 (amyloid beta-protein inhibitor) GlaxoSmithKline idalopirdine(serotonin 6 receptor antagonist) Lundbeck & Otsuka Pharmaceuticalimmune globulin Grifols USA INP-102 intranasal Impel NeuroPharmaJNJ-54861911 (BACE inhibitor) Janssen Research & Development & ShionogiLY3002813 (N3pG-amyloid beta mAb) Eli Lilly MEDI1814 (anti-amyloid betamAb) MedImmune memantine transdermal patch Corium International MER 5101(vaccine with beta-amyloid protein MerciaPharma fragment) MK-7622(muscarinic M1 receptor modulator) Merck MSDC-0160(mTOT modulator)Metabolic Solutions Development NGP 555 (amyloid precursor proteinsecretase NeuroGenetic Pharmaceuticals modulator) NIC-515 (amyloidprecursor protein secretase Humanetics inhibitor) NTC-942 (serotonin 4receptor agonist) Nanotherapeutics PF-05251749 Pfizer PF-06648671 PfizerPF-06751979 Pfizer pioglitazone (insulin sensitizer) TakedaPharmaceuticals piromelatine (melatonin agonist) Neurin PharmaceuticalsPosiphen ® (R-phenserine) QR Pharma rilapladib (Lp-PLA2 inhibitor)GlaxoSmithKline RVT-101 (serotonin 6 receptor antagonist) AxovantSciences SAR228810 (anti-protofibrillar AB mAb) Sanofi US solanezumab(amyloid beta protein inhibitor) Eli Lilly SUVN-502 (serotonin 6receptor antagonist) Suven Life Sciences SUVN-D4010 (serotonin 4receptor agonist) Suven Life Sciences T-817MA (amyloid beta-proteininhibitor) Toyama Chemical T3D-959 (PPAR-delta/gamma agonist) T3DTherapeutics TGF-beta agonist Stanford University & SRI Bioscience TPI287 (next-generation taxane) Cortice Biosciences TRx0237 (tau proteinaggregation TDP-43 TauRx Pharmaceuticals aggregationinhibitor)inhibitor/ UB-311 (amyloid beta-protein inhibitor vaccine)United Biomedical verubecestat (MK-8931) (BACE1 protein inhibitor) MerckVX-745 (p38 mitogen-activated protein kinase EIP Pharma inhibitor)

Models that are trained based on the parameters to determine whether apatient is cognitively impaired and to discriminate degrees of cognitiveimpairment can also be used in methods of setting the dosage of ananti-cognitive impairment therapeutic agent in a patient havingcognitive impairment. In typical embodiments, the method comprisesdetermining the degree of cognitive impairment, and then setting thedosage of the anti-cognitive impairment therapeutic agent based on thedetermined degree of the patient's cognitive impairment.

Models that are trained based on the parameters to determine whether apatient is cognitively impaired and to discriminate degrees of cognitiveimpairment can also be used in methods of titrating the dosage of ananti-cognitive impairment therapeutic agent in a patient havingcognitive impairment. In typical embodiments, a first determination anda second determination of the degree of cognitive impairment aredetermined at a spaced interval during which interval the patient hasbeen receiving an anti-cognitive impairment therapeutic agent at a firstdosage level, and the dosage is increased to a second dosage level ifthe degree of cognitive impairment has increased between the first andsecond determinations.

VI. Model Performance & Observations

Additional analysis may be done to evaluate the performance of a modelonce the model has been developed. To evaluate the example modelsdescribed herein, the highest scoring good candidate parameters wereused to predict the MMSE score of each test patient. Those calculationswere performed using the entire dataset and also using cross-validation.In cross validation, one of the test patients is left out and the modelis trained using all of the remaining test patients. The trained modelis then used to predict the MMSE score of the left-out test patient.That evaluation was done for each test patient as the left-out testpatient.

In the one-step classification model, the left-out test patient wasclassified directly as a normal test patient or an AD test patient,without predicting an MMSE score. In the two-step model, the left-outtest patient was classified as a normal test patient or an AD testpatient based on the predicted MMSE score. Referring to FIG. 6A and FIG.6B, the seven candidate parameter Example ADD Model 1, implemented as alinear model as described above without using LOOCV, provides a verygood prediction of the MMSE score (r=0.94, p<0.001) for the left-outtest patient. In this simulation of a clinical environment in which thestatus of the test patient is unknown, the model was able to perfectlydiscriminate between normal test patients and AD test patients.Referring to FIG. 6C and FIG. 6D, the eight candidate parameter ExampleADD Model 2 using LOOCV, implemented as a non-linear model as describedabove, is still able to perfectly distinguish between normal and AD, butdoes not predict the MMSE score (r=0.88, p<0.001) as well as Example ADDModel 1. Specifically, a Random Forest Regressor was trained for thenon-linear model using all good candidate parameters of the testpatients and predicted the MMSE score of the left-out test patient. Inother words, when using a leave-one-out cross validation with thenon-linear model, the reliable and stable model parameters predictwhether the left-out test patient was normal or AD with 100% accuracy(perfect sensitivity and specificity).

Although the model was developed using normal test patients and AD testpatients, the model may allow for the identification of test patientswith an intermediate level of cognitive function (“minimal cognitiveimpairment” or “MCI”) between that of normal test patients and that oftest patients with AD.

In the MEG data described herein, it appears that the peak A 90 issetting the “time lock” of the first note of the response for the peak B91. The peak B 91 is then generated, with it being suspected that thepeak B 91 is shared by signal connectivity with the frontal cortex andthe peak C 92 then helps to characterize the peak B 91. A missing peak C92 may be associated with a prolonged peak B 91 but is not a requirementfor a correctly timed peak B 91.

The model may be used to detect temporal changes in a magnetic corticalsurface map as a result of application of one or more controlled stimulito a human patient as described herein. The results may be used to givea better understanding of the correlation between stimuli and humanbrain activity.

VII. Additional Cognitive Impairment Models

VII.A. Summary

Additional embodiments beyond discussed with respect to the ADD modeland examples of Section IV above are also possible. For compactness ofdescription, the following examples described only those aspects thathave changed from previous examples, unless otherwise stated, examplepatient data, model development including sensor selection, parameterselection, model training, and inference is the same as discussed abovein Sections III and IV.

For convenience of description, the models of Section V may be referredto as Cognitive Impairment (CI) models to illustrate the applicabilityof the model to any disease that affects cognitive impairment beyondAlzheimer's Disease. In practice, both the previous ADD models ofSection IV and the CI models of this section both function to identifypresence and progression of cognitive diseases. However, distinguishingthe CI models of this section from the ADD models of the prior examplesis also useful for conveniently distinguishing between the models. Inone specific embodiment, both CI and ADD models may characterize acognitive impaired subject as someone having an MMSE score below 26.Other embodiments may use other tests other than MMSE and otherthresholds as baselines against which to label cognitive impairment.

The CI models of this section include several aspects that vary versusthe examples in the prior sections. First, they include additionalwithin-day variability features that represent and capture evidence ofinstability in short-term cognitive function of individuals withcognitive impairment. Implicit in these features is that multiple scansacquired for a patient are useful in evaluating cognitive function.Second, they exclude features that were not stable across multiple(across-day) visits by an individual, thus removing features that werenot reliable indicators of cognitive impairment. They also includecontralateral channel features, in addition to ipsilateral channelfeatures used in the ADD models.

VII.B. Sensor Selection

While in the ADD models the sensor from which features were created wasselected based on a stability metric, the current models achievesuperior results by selecting the sensor based on a metric of signaldeflection. Specifically, the algorithm chooses the channel from a poolof a plurality (e.g., 12) of channels (ipsilateral or contralateral)that has the highest absolute signal deflection in the heat map, withina time window (e.g., 50 to 250 ms) (herein referred to as the mostDefmethod). The example 50 ms to 250 ms time window was selected because itcomfortably accounts for both A and B peaks in most subjects, regardlessof latency drifts across epochs, or inter-subject variability. In otherembodiments, other sensor selection methods (e.g., sensor stability asdiscussed previously) may be used in place of the mostdef method.

VII.B. Within-Day Variability Features

The inventors recognized that the within-day variability for manyfeatures correlated with cognitive function. Computing the absolutedifference between two scans of a patient captured on the same dayillustrated this in test data. The difference in time within the daybetween the two scans may vary. For the example data discussed below,the two scans were about 45 minutes apart.

FIG. 7 illustrates a correlation matrix between ipsilateral features(vertical) and different psychiatric tests for evaluating cognitiveimpairment (horizontal), according to one embodiment. CI model featuresindicating information about same-day variability have the prefix“sameDayABSDiff.” A full key for abbreviations in the figures can befound in Sections VII.X. and VII.Y below.

Within FIG. 7 , the value of each cell illustrates the p-value ofPearson correlation tests between one of the features and one of themany known tests for cognitive impairment. The darker the color of thecell, the higher the association between the feature and the test. TheADD models discussed in prior sections focused on the first column (MMSEscore), and the last one (group separation between CI and NV), but FIG.7 illustrates that features in both models are also related to othertests commonly used to evaluate cognitively impaired patients.

FIGS. 8A, 8B, and 8C illustrate scatterplots of within-day featurevariability for three possible model features, according to oneembodiment. FIG. 8A specifically plots MMS for a number of the testpatients against within-day variability (samedayABSdiff) in the numberof A or B peaks for that patient. FIG. 8B specifically plots MMS for anumber of the test patients against within-day variability in the areaunder the curve for A peaks for that patient. Both FIGS. 8A and 8Billustrate that there is a significant amount of within-day variabilityfor these features for patients exhibiting cognitive impairment (e.g.,MMS<26) as compared to NV patients.

FIG. 8C illustrates a scatter plot of same-day feature variability inarea under the curve for C peaks plotted against MMSE score, accordingto one embodiment. FIG. 8C specifically illustrates an example featurewhere NV patients have high same-day variability whereas CI patientshave low within-day variability.

In one embodiment of the ADD model discussed in Section IV above, asecond scan acquired on the same day is used to establish featurereliability (for example, using Bland-Altman plots). Alternately, in oneembodiment of the CI model, the second scan on the same day is insteadused to compute feature variability, and as a result feature reliabilityas calculated for the ADD model is not used in this embodiment of the CImodel. Further, one or more of the features of the CI model may be afeature that quantifies the variability of scan data (e.g., number of Apeaks) which itself may be another feature in the model.

VII.C. Restricting Same Scan Features to Ones Stable Across Visits

Further, the inventors recognized that while adding within-dayvariability features enhanced model performance, many features derivedfrom single scans still provided meaningful boosts to model performance.FIG. 9 illustrates a scatterplot of one such example feature where theaverage onset of the B peak shows an inverse correlation with apatient's MMS score, according to one embodiment.

However, not all features were sufficiently stable across separate testson separate days for NV patients as well as CI patients to meritinclusion in the model. In order to make sure features included in amodel were stable across evaluations, the correlation between featureswas measured across separate MEG scans on separate days. The number ofdays between scans may vary, but is generally short compared to thetypical scale of the cognitive disease being studied, which aregenerally on the order of months if not years. For the example datadiscussed below, the two scans were about two weeks apart.

In one embodiment, a first vector was constructed using a separate datapoint from each of the test patients for a given feature for a firstvisit and scan (visit 1, scan 1). A second vector was constructed usingthe same data points of the same feature for the set of test patientsfor a second visit and scan (visit 2, scan 2). Features considered forinclusion in a model were those that had a statistically significantcorrelation (p<0.05, corrected using False Discovery Rate at q<0.05)between the two vectors. Those of skill in the art will appreciate thatmany other similar tests may be used to evaluate which features to carrythrough to a model based on inter-day feature stability.

VII.D. Adding Contra-Lateral Features

Further, the inventors recognized that model performance could beimproved by including MEG sensor data from contralateral to the ear thatreceived the auditory stimulation, in addition to sensor data fromsensors ipsilateral to the ear that received the auditory stimulation.

FIG. 10 illustrates a correlation matrix between contralateral features(vertical) and different psychiatric tests (horizontal), according toone embodiment. The features and psychiatric tests in FIG. 10 are thesame as in FIG. 7 . Comparing FIG. 7 (ipsilateral features) and 10(contralateral features) illustrates that the two different sets offeatures have a different pattern of related psychiatric tests that arerelated. In particular, while the tests on the left of the matrix aremore related to ipsilateral features, tests on the right are morerelated to the contralateral features. As a specific example,contralateral features correlate well with ReyCo and MBAR, bothalternate tests of higher cognitive function and abstract reasoning.

Because of this complementary pattern, one embodiment of the CI modelincludes at least one feature from at least one contralateral sensorchannel in addition to at least one feature from an ipsilateral sensorchannel. In another embodiment, a CI model may be built using featuresbased on solely contralateral sensor channels.

VII.E. Example CI Models

In one embodiment, one or more linear CI models are constructed. Each CImodel can be constructed to include different subsets of features fromeach other model based on how well they predict MMSE for a test set ofpatients. The linear CI models output a predicted MMSE score which canbe used to classify between CI and NV groups by comparing against athreshold MMSE score (e.g., 26). In other embodiments, other CI modelsmay be constructed including different features. The CI models may belinear or non-linear functions of the feature weights and values.Additionally, the CI models may be constructed to predict one or moredifferent psychiatric test values, such as any of the psychiatric testslisted in Section VII.X. below.

The CI models were evaluated in a leave-one-out cross validation (LOOCV)framework to select up to 5 features. The CI models used features fromboth ipsilateral and contralateral sides. In this specific embodiment,two sensor channels were used: one in each side of the helmet based onthe mostDef method. Although this approach increases the number offeatures used in total, it is advantageous as it likely capturesdifferent types of information. In this embodiment, the CI models weretrained on 19 out of 20 patients, and the MMSE score was predicted onthe remaining patient. The predicted score was used to place the patientin either the NV or CI group. This process for each patient in theleave-out position to produce predictions for all patients.

In other embodiments, further features beyond 5 may be used. Generally,the number of features is restricted to avoid overfitting, however inpractice additional or fewer features may be used based on a variety offactors, such as the psychiatric tests used for training and inference,the amount of training data available, and the sensors used to collectdata (e.g., contralateral, ipsilateral). Training more than one CI modelcan be advantageous as it provides multiple predictions/scores that canbe aggregated (e.g., average, median) or provided as part of acomprehensive report on the presence or absence of cognitive impairmentin a patient.

FIGS. 11A and 11B plot predicted and actual MMSE scores for two types ofdual-channel CI models, according to one embodiment. FIG. 11Aillustrates an example CI model where the candidate features includedonly features significantly correlated to MMS (p<0.05, for a total of 16features). Stated differently, the example CI model of FIG. 11A choosesthe best linear combination of five or less features among all featuressignificantly correlated to MMS. Example CI model 1 selected features[sameDayABSDiff_blueA.ipsi, sameDayABSDiff_blueC.contra, sameDayABSDiffdurationB: variability.ipsi, sameDayABSDiff_pctA+B.ipsi, andsameDayABSDiff_strongAB.ipsi], and the predicted scores using LOOCVachieved 90% a classification accuracy (mean-squared error 4.28).

FIG. 11B, by contrast, illustrates an example CI model where featurescorrelated to any of the neuropsychiatric tests were included. Stateddifferently, the example CI model of FIG. 11B chooses the best linearcombination of five or less features among all features significantlycorrelated to any of the neuropsychiatric tests evaluated. In thisexample, this included features corresponding to any of the dark squaresin FIGS. 7 and 10 , for a total of 78 features. Example CI model 2 usedfeatures [latencyB: average.ipsi, sameDayABSDiff_ApctWindowGood.ipsi,sameDayABSDiff_amplitudeA: average.contra, sameDayABSDiff_blueA.ipsi,and sameDayABSDiff_strongA_Camp.contra] and achieved a classificationaccuracy of 100% (mean-squared error 1.96).

The results discussed herein, as well as the features chosen to be usedin the CI models are robust to exactly which channels were selected.Comparing the ADD and CI models, the two sets of models employ differentchannel selection techniques and different features, and correspondinglydifferent values of evoked responses. Although the CI models outperformthe ADD models in predictive performance, both types of models arepredictive. This is a both a reflection of the spatial resolution ofsingle sensors in MEG, and also that the processes described herein toare somewhat regional across the brain. This observation inform designedof the reduced sensor-count array discussed above, as precisepositioning of the device may strictly necessary for the models togenerate a predictive result.

VII.F. Example of Clinical Display

FIG. 12 illustrates a graphical user interface (GUI) for presenting theresults of scans and the prediction of a CI model, according to oneembodiment. The graphical user interface is visually presented on adisplay of a computing device. The GUI may illustrate color-coded epochdata (heat maps) and may also show evoked (averaged) response (e.g.,blue for positive signals, red for negative signals, saturation of colorcorresponding to amplitude). The heatmaps can be sorted based ondifferent peaks using the buttons on the bottom of the display. The GUImay illustrate the sensor channels used, whether they are ipsilateral orcontralateral, the features correspond to each sensor, the valuecorresponding to each feature, and the normal range for each featurevalue. Separate tabs in the GUI may permit switching between the data ofdifferent runs, or switch to showing features based on within-dayfeature variability. Interactive buttons permit transitioning betweendifferent views of the GUI, such as between runs or features.

Another button on the GUI opens display options, examples of whichinclude but are not limited to: list of features to show (with option toget back to defaults), list of annotations to show (e.g. vertical linesfor onset, offset, latency, with option to get back to defaults),whether or not to display the CI model prediction, thresholds tohighlight features in the table in red. For example, outside the range,less than X % of being in the normal distribution, etc., a show “moredetails” button. Further, each feature in the table can have a “moredetails” button next to it, that when interacted with displays thesingle feature distribution, with a short description of the feature.

Other variations on the GUI are envisioned, and may include any aspectof data or input discussed in this document.

VII.Y. CI Model Feature Key

The following are a non-exhaustive list of features that may be includedin a CI model. Different embodiments of a CI model may use differentones of these features in combination. These features may be in additionto or in place of the ADD model features discussed in Sections III andIV above.

-   -   sameDayABSDiff[FEATURE]: Absolute difference between the values        for FEATURE in the two scans acquired on the same day, where        FEATURE is another feature from the CI model (such as any of the        below) or another feature such as an ADD model feature.    -   pctA*B*C: Percentage of epochs with peaks A, B, and C.    -   blueA: Area under the A peak curve (e.g., amount of “blue” in        heatmaps between onset and offset of A peak).    -   pctA*B: Percentage of epochs with A and B peaks only.    -   pctA: Percentage of epochs with A peaks only.    -   strongAB: Number of epochs with B peaks in the epochs with        strong A peaks.    -   blueC: Area under the C peak curve (e.g. amount of “blue” in        heatmaps between onset and offset of C peak).    -   latencyB: average: Average latency in B peak. The average of all        evoked responses (e.g. as depicted in FIG. 2B) is used to obtain        the latency of each peak. That curve can also be obtained using        multiple bootstraps (sampling with replacement) of the        individual epochs. So, for each bootstrapped curve, one estimate        of latency is obtained. the “: average” feature is the mean of        that distribution, and the “: variability” feature is the        standard deviation. This is applicable to the other features        below with “: average” and “: variability in their name, except        with that feature value rather than latency as is the case here.    -   onsetB: variability: Variability in the timing onset of the B        peak.    -   durationB: average: Average duration (offset minus onset) of the        B peak.    -   onsetB: average: Average onset for B peak (e.g. time point where        signal surpasses 2 standard deviations of the average baseline        signal).    -   latencyAsd: Standard deviation of the latency of A across all        epochs.    -   amplitudeA: average: Average amplitude of the A peak.    -   latencyBsd: Standard deviation of the latency of B across all        epochs.    -   offsetB: average: Average offset for B peak (e.g. time point        where signal returns to levels below 2 standard deviations of        the average baseline signal).    -   ApctWindowGood: Metric of A peak timing variability; the more of        the onset to offset window has the peak color, the closer to 1        the value of the feature.    -   blueC: Area under the C peak curve (e.g., the amount of “blue”        in heatmaps between onset and offset of C peak).    -   blueRatio: Area under the A curve divided by the area under the        C curve.    -   BpctWindowGood: Metric of B peak timing variability; the more of        the onset to offset window has the peak color, the closer to 1        the value of the feature.    -   nFeatureNaNs: How often the algorithm was unable to calculate a        given feature. Any other feature from the CI models or ADD        models may be used. This feature acts as a proxy for MEG signal        quality, so if this feature has a low value it is indicative of        a process error in testing the patient.

Cognitive Test Table Variable Test name mms mini mental state - standardmms7 mini mental state - using serial sevens mmsw mini mental state -using “world” backwards wrec verbal learning trial one wrec2 verballearning trial two wrec3 verbal learning trial three wrecde verbaldelayed recall targets recognition memory hits foils Recognition memoryfalse alarms reyim Visual memory immediate reyde Visual memory delayedlogmema1/2ss Paragraph recall-scaled score boston1/3 Boston naming testsfluen Semantic fluency fluenf Letter fluency-F fluena Letter fluency-Afluens Letter fluency-S spanfbasal digit span forward spanbbasal digitspan backward trailas Trail making A time trailbs Trail making B timetrailbe Trail making B errors clockd clock drawing reyco visual figurecopy blkdsn block design boston60 60 item Boston naming bos60phone 60item Boston naming with cues bnt60ss 60 item Boston naming scaled scorestpw Stroop test words stpc Stroop test colors stpcw Stroop testinterference stroopintss Stroop test scaled score trailae Trail making Aerrors bos60seman 60 item Boston naming semantic

VII. Additional Considerations

Similar methodologies may be developed that may be useful in monitoringfor other specific medical conditions or generally monitoring humanbrain function. The model described herein analyzes the MEG datacollected after an auditory stimulus, including the relative extent ofbrain activation/excitation and subsequent response to the activation.The MEG data for the model may come from only a small number of theSQUID sensors generally from as few as a single SQUID sensor up to aboutsix, although a full set of SQUID sensors (e.g., 306 sensors) may alsobe used.

While the invention has been described with reference to one or moreembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the invention without departing from theessential scope thereof. Therefore, it is intended that the inventionnot be limited to the particular embodiment disclosed as the best modecontemplated for carrying out this invention, but that the inventionwill include all embodiments falling within the scope of the appendedclaims. In addition, all numerical values identified in the detaileddescription shall be interpreted as though the precise and approximatevalues are both expressly identified.

What is claimed is:
 1. A method comprising: accessing a set of epochs ofelectrical data of responses of a brain of a test patient to a pluralityof auditory stimulus events; identifying different types of peaks in theset of epochs, the different types of peaks including at least a firsttype of peaks; selecting a subset of epochs that are identified ashaving the first type of peaks; determining a plurality of parametervalues by analyzing individual epochs of the electrical data, at leastfirst parameter values determined based on epochs captured by anelectrical sensor that is located ipsilateral to where the auditorystimulus events occurred on the test patient; at least second parametervalues determined based on epochs captured by an additional electricalsensor that is located contralateral to where the auditory stimulusevents occurred on the test patient; and at least the first parametervalues or the second parameter values determined from the subset ofepochs that are identified as having the first type of peaks; inputtingthe parameter values into a model that is trained based on theparameters; and providing a determination as to whether the test patientis cognitively impaired based on the plurality of parameter values. 2.The method of claim 1, wherein parameters of the model were selected ashaving variability values below a threshold across multiple scans onseparate days for a set of training patients.
 3. The method of claim 1,further comprising: accessing a second set of epochs of electrical dataof responses of the brain of the test patient to the auditory stimulusevents captured on a same day as the set of epochs.
 4. The method ofclaim 3, wherein determining one of the parameter values comprises:determining one of the parameter values based on an absolute differencebetween a first value for the parameter in the set of epochs and asecond value for the parameter in the second set of epochs.
 5. Themethod of claim 4, wherein the first and the second values for theparameter are based on the epochs captured by the electrical sensorlocated contralateral to the auditory stimulus events.
 6. The method ofclaim 1, wherein the first and the second values for the parameter arebased on a difference between a first variability for type-B peaks inthe set of epochs and a second variability for type-B peaks in thesecond set of epochs.
 7. The method of claim 6, wherein the first andthe second values for the parameter are based on the epochs captured bythe electrical sensor located ipsilateral to the auditory stimulusevents.
 8. The method of claim 1, wherein the first and the secondvalues for the parameter are based on: identifying a strong subset ofthe epochs having a strongest amplitude for type-A peaks relative toother epochs in the set; and determining the first and the second valuesbased on a count of the epochs in the strong subset having type-B peaks.9. The method of claim 1, wherein the first and the second values forthe parameter are based on a time extent of onset to offset for epochswith type-A peaks.
 10. The method of claim 1, wherein the first and thesecond values for the parameter are based on: identifying a strongsubset of the epochs having a strongest amplitude for type-A peaksrelative to other epochs in the set; and determining the first and thesecond values based on amplitudes of type-C peaks in the strong subsetof epochs.
 11. The method of claim 1, further comprising: determiningone of the parameter values based on an average latency in type-B peaks.12. The method of claim 1, wherein the model is a one-step modelconfigured to classify the test patient as cognitively impaired ornot-cognitively impaired.
 13. The method of claim 1, wherein the modelis a two-part model, the model comprising a first part configured topredict a score for test patient, and a second part configured toclassify the test patient as cognitively impaired or not-cognitivelybased on the predicted score.
 14. The method of claim 1, wherein themodel was trained using at least one of a linear model, a random forestmodel, a gradient boosting model, a support vector machine (SVM) model,a linear SVM model, a radial basis function kernel SVM, a linearregression, and a logistic regression.
 15. The method of claim 1,further comprising: transmitting the plurality of auditory stimulusevents; and recording the set of epochs with the electrical sensor. 16.The method of claim 1, wherein the set of epochs comprises at least 250epochs.
 17. A computer system comprising: a computer processor; a memorystoring a set of instructions that when executed by the computerprocessor causes the computer processor to: access a set of epochs ofelectrical data of responses of a brain of a test patient to a pluralityof auditory stimulus events; identify different types of peaks in theset of epochs, the different types of peaks including at least a firsttype of peaks; select a subset of epochs that are identified as havingthe first type of peaks; determine a plurality of parameter values byanalyzing the epochs of the electrical data, at least first parametervalues determined based on epochs captured by an electrical sensor thatis located ipsilateral to where the auditory stimulus events occurred onthe test patient; at least second parameter values determined based onepochs captured by an additional electrical sensor that is locatedcontralateral to where the auditory stimulus events occurred on the testpatient; and at least the first parameter values or the second parametervalues determined from the subset of epochs that are identified ashaving the first type of peaks; and inputting the parameter values intoa model that is trained based on the parameters; and provide adetermination as to whether the test patient is cognitively impairedbased on the plurality of parameter values.
 18. The computer system ofclaim 17, wherein parameters of the model were selected as havingvariability values below a threshold across multiple scans on separatedays for a set of training patients.
 19. The computer system of claim17, wherein the model is a two-part model, the model comprising a firstpart configured to predict a score for test patient, and a second partconfigured to classify the test patient as cognitively impaired ornot-cognitively based on the predicted score.
 20. The computer system ofclaim 17, wherein the model was trained using at least one of a linearmodel, a random forest model, a gradient boosting model, a supportvector machine (SVM) model, a linear SVM model, a radial basis functionkernel SVM, a linear regression, and a logistic regression.